{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization\n",
    "\n",
    "Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!\n",
    "\n",
    "**You will learn to:** Use regularization in your deep learning models.\n",
    "\n",
    "Let's first import the packages you are going to use.\n",
    "\n",
    "---\n",
    "\n",
    "欢迎来到本周的第二个任务。深度学习模型具有如此大的灵活性和能力，以至于当训练数据集不够大的时候，能导致严重的问题：过拟合。它能确保在训练集上表现良好，但是训练出的网络并不能推广到它从未见过的新样本上！\n",
    "\n",
    "您将学习：在深度学习模式中使用正则化。\n",
    "\n",
    "我们先导入你要使用的软件包。\n",
    "\n",
    "---\n",
    "\n",
    "> 我觉得这一节的重点应该是理解各个正则化方法的原理，以及它们的优缺点，而不是去注重算法实现的具体末节"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec\n",
    "from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import scipy.io\n",
    "from testCases import *\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Problem Statement**: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head.\n",
    "\n",
    "问题陈述：您刚刚被法国足球公司聘为AI专家。他们希望你推荐法国守门员应该踢球的位置，这样法国队的球员可以用头打。\n",
    "\n",
    "<img src=\"images/field_kiank.png\" style=\"width:600px;height:350px;\">\n",
    "<caption><center> <u> **Figure 1** </u>: **Football field**<br> The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head（守门员将球踢向空中，各队的球员正在拼命用头打球） </center></caption>\n",
    "\n",
    "\n",
    "They give you the following 2D dataset from France's past 10 games.\n",
    "\n",
    "他们为您提供法国过去10场比赛中的以下二维数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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FECKf9u/fz6hRo6hUvh0Najyf/ZpNqYGcubyZqVOnUq1aNZ5//vkii0kphcb910jpNJ3R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\n2P8Df5opvJFCAZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fc6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_X, train_Y, test_X, test_Y = load_2D_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each dot corresponds to a position on the football field where a football player has hit the ball with his/her head after the French goal keeper has shot the ball from the left side of the football field.\n",
    "- If the dot is blue, it means the French player managed to hit the ball with his/her head\n",
    "- If the dot is red, it means the other team's player hit the ball with their head\n",
    "\n",
    "**Your goal**: Use a deep learning model to find the positions on the field where the goalkeeper should kick the ball.\n",
    "\n",
    "---\n",
    "\n",
    "每个点对应于法国守门员在足球场左侧击球之后，其他运动员用头将球击中的足球场上的位置。\n",
    "\n",
    "- 如果这个点是蓝色的，这意味着这个法国球员设法用他/她的头击球\n",
    "- 如果这个点是红色的，这意味着另一个队的球员用头撞球\n",
    "\n",
    "**你的目标**：使用深度学习模式来找到守门员踢球的场地。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of the dataset**: This dataset is a little noisy, but it looks like a diagonal line separating the upper left half (blue) from the lower right half (red) would work well. \n",
    "\n",
    "You will first try a non-regularized model. Then you'll learn how to regularize it and decide which model you will choose to solve the French Football Corporation's problem.\n",
    "\n",
    "---\n",
    "\n",
    "**分析数据集**：这个数据集有点嘈杂，但貌似用一条对角线能区分开左上角（蓝色）与右下角（红色）的数据，效果还不错。\n",
    "\n",
    "你将首先尝试一个非正则化的模型。然后，您将学习如何正规化，并决定选择哪种模式来解决法国足球公司的问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Non-regularized model\n",
    "\n",
    "You will use the following neural network (already implemented for you below). This model can be used:\n",
    "- in *regularization mode* -- by setting the `lambd` input to a non-zero value. We use \"`lambd`\" instead of \"`lambda`\" because \"`lambda`\" is a reserved keyword in Python. \n",
    "- in *dropout mode* -- by setting the `keep_prob` to a value less than one\n",
    "\n",
    "You will first try the model without any regularization. Then, you will implement:\n",
    "- *L2 regularization* -- functions: \"`compute_cost_with_regularization()`\" and \"`backward_propagation_with_regularization()`\"\n",
    "- *Dropout* -- functions: \"`forward_propagation_with_dropout()`\" and \"`backward_propagation_with_dropout()`\"\n",
    "\n",
    "In each part, you will run this model with the correct inputs so that it calls the functions you've implemented. Take a look at the code below to familiarize yourself with the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (input size, number of examples)\n",
    "    Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (output size, number of examples)\n",
    "    learning_rate -- learning rate of the optimization\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    print_cost -- If True, print the cost every 10000 iterations\n",
    "    lambd -- regularization hyperparameter, scalar\n",
    "    keep_prob - probability of keeping a neuron active during drop-out, scalar.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learned by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = []                            # to keep track of the cost\n",
    "    m = X.shape[1]                        # number of examples\n",
    "    layers_dims = [X.shape[0], 20, 3, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    parameters = initialize_parameters(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        if keep_prob == 1:\n",
    "            a3, cache = forward_propagation(X, parameters)\n",
    "        elif keep_prob < 1:\n",
    "            a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob)\n",
    "        \n",
    "        # Cost function\n",
    "        if lambd == 0:\n",
    "            cost = compute_cost(a3, Y)\n",
    "        else:\n",
    "            cost = compute_cost_with_regularization(a3, Y, parameters, lambd)\n",
    "            \n",
    "        # Backward propagation.\n",
    "        assert(lambd==0 or keep_prob==1)    # it is possible to use both L2 regularization and dropout, \n",
    "                                            # but this assignment will only explore one at a time\n",
    "        if lambd == 0 and keep_prob == 1:\n",
    "            grads = backward_propagation(X, Y, cache)\n",
    "        elif lambd != 0:\n",
    "            grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n",
    "        elif keep_prob < 1:\n",
    "            grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 10000 iterations\n",
    "        if print_cost and i % 10000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            costs.append(cost)\n",
    "    \n",
    "    # plot the cost\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (x1,000)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the model without any regularization, and observe the accuracy on the train/test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6557412523481002\n",
      "Cost after iteration 10000: 0.16329987525724213\n",
      "Cost after iteration 20000: 0.13851642423245572\n"
     ]
    },
    {
     "data": {
      "image/png": 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rSZLGmmFtGFwfVJIkjTXD2jDYDSpJksaaYW0Y7AaVJEljzbA2DHaDSpKksWZY\nG4beXnjoIXjssW7XRJIkTRSGtWFwYlxJkjTWDGvDYFiTJEljzbA2DK4PKkmSxpphbRgaLWv33NPd\nekiSpInDsDYMu+wCCxbA294GZ5wBGzZ0u0aSJGl7Z1gbhgj4+c/hjW+ED34Q9tsPLrgANm/uds0k\nSdL2yrA2THPmwCc+AStWwBFHwEknwdOfDt//frdrJkmStkeGtRHad1/4+tfhJz+BqVPhmGPgj/8Y\nrr++2zWTJEnbE8PaVjrqKLjiihLcVqyApz2tPNO2alW3ayZJkrYHhrVREAGvfnVpVTvrrBLc9t0X\nPvIRWLeu27WTJEnjWW3CWkScGhG3RsSGiLgyIp4xyL6viIhLIuLeiFgTEVdExLFjWd92pk6F97wH\nbr4Z3v52+OhHyyCEL34RNm3qdu0kSdJ4VIuwFhEnAGcBpwGHAb8ELo6I3gEOeTZwCfAiYCFwOfDt\niDhkDKo7pF13hTPPhBtugGc/G970JjjsMLjkkm7XTJIkjTe1CGvAEuBzmXlBZt4AnAKsB97YbufM\nXJKZ/5iZyzLz5sz8APBr4CVjV+WhLVgAS5fCz34Gs2bBcceV7X//t9s1kyRJ40XXw1pETAYWAZc1\nyjIzgUuBIzs8RwA7Aw9sizpurcMPhx/+EL75Tbj1Vjj00NLadvfd3a6ZJEmqu66HNaAXmAS0LuJ0\nDzC/w3P8FbAjcOEo1mtURcDLXw7XXQef/CRcdFF5nu0f/gEeeaTbtZMkSXVVh7C2VSLitcDfAn+S\nmfd3uz5DmTwZ3vlO+PWv4S1vgb/9WzjooBLeMrtdO0mSVDc7dLsCwP3AJmBeS/k8YNDZyiLiNcDn\ngVdn5uWdXGzJkiXMnDmzX9nixYtZvHhxxxUeDbNmwTnnwFvfWkaQvuxlcOyxZXWEAw8c06pIkqSt\nsHTpUpYuXdqvbM2aNaN2/sgaNOdExJXAzzLz3dX7AG4HPpmZZw5wzGLgX4ATMvM/O7jGQmDZsmXL\nWLhw4ehVfhRkwre/De99L9x2W2l5O+20EugkSdL4s3z5chYtWgSwKDOXb8256tINejbwlog4MSIO\nAD4LzADOB4iIMyLiS42dq67PLwF/Afw8IuZV2y5jX/WtFwEvfWl5nu0jH4EvfKE8z/aFLzg/myRJ\nE10twlpmXgj8JXA6cDVwMHBcZt5X7TIf2LPpkLdQBiV8Gri7afvEWNV5W5g6Fd7/frjpJjj++NJF\n+oxnwI9zf9oAAAAXCElEQVR/3O2aSZKkbqlFWAPIzHMzc+/MnJ6ZR2bmL5o+Ozkzn9/0/nmZOanN\n1nZetvFm993hggvKmqOTJsGzngWvfS3ceWe3ayZJksZabcKatnTkkWVC3S9+Eb7/fdh//7KE1YYN\n3a6ZJEkaK4a1muvpgZNPLl2j73gHnH46PPWp8O//7lQfkiRNBIa1cWKXXcp6o7/6VQlrr3oVvOAF\ncO213a6ZJEnaluowz5qGYb/94Dvfge9+t8zPduihZZH4mTNLoGt9bVfWeN155/JMnCRJqi/D2jj1\nR39UWta+8IWyMPyaNfDww/Cb3/T93XgdrLt0p536Aty8ebDXXrD33mVr/L3HHmXlBUmSNPYMa+PY\nlClw6qmD75MJ69ZtGeCa/16zpmyrVsGNN8Ill8Bvf9t3jp6eEtjaBbm994Y99yx1kSRJo8+wtp2L\nKK1nO+0ET3xi58dt3Ai33w4rV5ZVFVauLNstt5SRqXff3ddiF1GmG2mEuAMPhKOOgsMPL9eVJEkj\nZ1hTW9Omlefj9tuv/eePPAJ33NE/yDW273yntNT19MDBB5cpSI46qmwLFpRwJ0mSOmNY04hMnQr7\n7lu2Vps3w4oV8NOflol9v/99+MxnymdPeEJfcDvySFi0CKZPH9u6S5I0nhjWNOp6euCgg8r25jeX\nstWr4cor+wLchz4E69eXgQuHHdY/wO2xR1erL0lSrRjWNCbmzIEXv7hsAI8/XuaIu+KKEuC+9S34\nRLWy6557ltB28MHQ2wuzZ/dtc+aU1x13tDtVkjQxGNbUFTvsUFrUDjusb0TrqlV9LW9XXAGXXQYP\nPli6VVtNntw/vLWGuday3t6y2eUqSRpvDGuqjfnz4RWvKFvD5s1lepEHHujbVq/u/75RduON/cs2\nbdryGjNm9A9vvb1bvm8tmzZt7O6BJEmtDGuqtZ4emDWrbPvs0/lxmfC735UQ19juv79va7xftaos\n4dUof+yxLc+1444lvD3hCWWKkic+sf+2xx7ldZddRu97Q5k+5a674M47y3bHHf3/vu++MjXKrrv2\n32bN2rKseZsxwy5kSRpPDGvaLkX0Lbe1YEFnx2TC2rX9Q11zsLvnnjK/3E9+UkLU6tX9j2/MZTfY\nNn9+WeJrw4a+INYcwprf33df//PvumsJhnvsAU9/OsydWyY8fvDBst12G1xzTd/79evbf8/Jk/sH\nu9mzy+oVu+1W6tf66lx5ktRdhjWpElHWS915584C3saNJbzddVf/7c474eab4Uc/Kp83t9b19JQA\n+dBD/c+1665lYMUee5TJhF/5yvJ3o+yJTxx+aHr00b7g1ro99FDf3w88UKZaufzysnLFo4/2P89O\nO5XQ1i7INb/29rrWrCRtC4Y1aYSmTStds4N1z27eXFrlmsPcgw+WcNMIY098YulqHW1TppQWs3nz\nOj8mswS5VatKcGt+bfx9/fXl9YEH+h87aVK5FwccUFaxOOCAvr9nzRrd7yZJE4lhTdqGenrKs25P\neEIZ+Vp3EX1dpAceOPi+jzwC997bF+buugt+/evSSnfhhaVbtrEk2bx5W4a4Aw4oYbWnZ9t/L0ka\nzwxrkkZk6tQStvbcs/3n69f3hbcbbijbFVfA+eeXLmQogx3233/LILfXXqM/YEOSxivDmqRtYsYM\nOOSQsjXbtAluv72Et+Ygd+ml/QdVDDVgY489Soudz8lJ2t4Z1iSNqUmTygCOBQvgRS/q/9nq1SW4\n3XFH/+f8brkF/ud/yoCN5gEQkyaVAQ4DBbqddir7TJpUJmJu9zrYZz09TnMiqfsMa5JqY84cOPro\ngT/fvLkEujvv3HIU7l13wQ9+0DeIY7RMmlRWvthxxxL+WrfhlE+ZMjp1mj69TN0yWueTVG+GNUnj\nRk9PCSlz5w4+YGP9+tIKt2FDWYd206ate924sczB19jWrev7e9Wq/u8bnzcGV2xLs2b1DWCZO7fv\n73bb7NkO5pDGK8OapO3OjBmw777du35mCYrNAW7t2vYrZIzE2rXl+b577+2/XXVVeb3vvi3ny2sO\nuo0AN39+3yCRJz2pvPocoFQ/hjVJGmURJTDOmFFC0VjLLGvqtoa55u2ee+Dqq8vzgc2rXeywQ3ne\nrzXENW9z5tTjWb6NG/svJdf4e8OGMqJ4333hyU8uE11L45lhTZK2MxEwc2bZnvKUwffNLM/43XFH\nGaV7xx39tyuvLM8INrcKTp/eP7zNm1emcpkype+1eeukbOrUUpfmtXzbBbHm13Xrtvw+O+xQztX8\n2bx5fcFt3337b7vuOjr3XNqWDGuSNIFFlOfZZs/ecpqVhs2bS0tca5C7444y/cqPflS6XVu3xx/f\nurpNnlyWMevtLa15vb1llYzm943Xxt+N+flWr4bf/KYs/fab35Ttppvgu98tYa9h9uyBg9zcufVo\nQZQMa5KkQfX0lCXSdtutrF3bqU2bSotcc4B75JEtQ12jLLMErkYA22mnkYelRoA74ogtP1uzpn+I\na4S6H/6wDExp2HnnEg6f/OQttz33LK140ljwpyZJ2iYa89hNm9btmvQ3cyYsXFi2VuvWlXn9mkPc\nzTfDv/1bWUJt06ay3w47lOfi2gW5ffbZNuv9bq3HHitd3g8+WILs7Nn1aDl89NFyPx2tPDDDmiRJ\nlR13hKc9rWytHnusPNfXCHCN7cc/hgsu6P+c3Pz5feFtwYLSSjd9egmu06f3bc3v233WLsBs2gQP\nPVS6eh94oPPXhx/uf56ZM/taDltfR7PlcNOm8tzjrbfCypXltXm7++7yfQ88EA46qGxPfWp53Wsv\nQxwY1iRJ6sjkyX0BrFVmGWXbGuR+/Wv47/8uQW7DhuFP3zJlSl9wmzoVfve7EtTazeM3fXrpQp49\nu687ecGC/mWzZ5f5+e69t7Qg3nxzef3610vL4ebN5VyNlsPmENf8d/PavZnlmcZG+GoNZLff3v/5\nxd1261vF5DnPgb33LkHyuuvK9s1vlu8JJTw3QlwjwB10UBmlPJFCnGFNkqStFFFGnc6bB0cdNfB+\nmzaV0LZxY3ltbJ2837ixhKTm4NV4nT27hLWt0dxy2BzkrrwSvvKVvgAFpRt1wYIy59/KlaWODXPm\nlAC2YEHpam4Es733LgFwqHpmlsEr11/fF+Cuu650Ra9dW/bZcccS3poD3EEHlRbB7THEGdYkSRoj\nkyb1LT9WN0O1HK5e3RfgGttOO/WFsUYga251G4mI0nL2pCfB8cf3r8Ptt28Z4r7xjb4u6J6eElzb\njRRu/N36ftas+gc8w5okSRpURF+4+cM/7F4d9tqrbC96UV/55s2lJe6668qzca1z8117bd/7NWu2\nPG+7gHf44fB//+/YfbehGNYkSdK41dPTF+KG8uijfYMuGqGu3QTMq1dv+3oPh2FNkiRNCFOmlJG6\n8+d3uybDU/NeWkmSpInNsCZJklRjhjVJkqQaM6xJkiTVmGFNkiSpxmoT1iLi1Ii4NSI2RMSVEfGM\nQfadHxFfiYgbI2JTRJw9lnXV4JYuXdrtKkwI3uex470eG97nseO9Hl9qEdYi4gTgLOA04DDgl8DF\nEdE7wCFTgXuBjwDXjEkl1TH/ERgb3uex470eG97nseO9Hl9qEdaAJcDnMvOCzLwBOAVYD7yx3c6Z\neVtmLsnMLwMPj2E9JUmSxlTXw1pETAYWAZc1yjIzgUuBI7tVL0mSpDroelgDeoFJwD0t5fcA42yO\nYUmSpNE1kZabmgawYsWKbtdju7dmzRqWL1/e7Wps97zPY8d7PTa8z2PHe73tNeWNaVt7rig9jt1T\ndYOuB16VmRc1lZ8PzMzMVwxx/OXA1Zn53iH2ey3wla2vsSRJUsdel5lf3ZoTdL1lLTMfi4hlwDHA\nRQAREdX7T47ipS4GXgesBDaO4nklSZJaTQP2puSPrdL1sFY5Gzi/Cm1XUUaHzgDOB4iIM4DdM/Ok\nxgERcQgQwE7A3Or9o5nZtp8zM1cDW5VsJUmShuGK0ThJLcJaZl5Yzal2OjCPMnfacZl5X7XLfGDP\nlsOuBhp9uAuB1wK3Afts+xpLkiSNja4/syZJkqSB1WHqDkmSJA3AsCZJklRjEyKsDWeReI1MRJwW\nEZtbtuu7Xa/xLiKeFREXRcRd1T19aZt9To+IuyNifUT8d0Ts2426jndD3euIOK/Nb/y73arveBQR\nfx0RV0XEwxFxT0R8MyL2a7Ofv+mt1Mm99je99SLilIj4ZUSsqbYrIuL4ln22+ve83Ye1ESwSr5H7\nFWWAyPxqe2Z3q7Nd2JEy4OYd9A2o+b2IeB/wTuCtwOHAOsrve8pYVnI7Mei9rvwX/X/ji8ematuN\nZwGfAv4QeAEwGbgkIqY3dvA3PWqGvNcVf9Nb5w7gfZSBjouA7wPfiogDYfR+z9v9AIOIuBL4WWa+\nu3oflJv7ycz8eFcrtx2JiNOAl2Xmwm7XZXsVEZuBl7dMHn03cGZmnlO934WyVNtJmXlhd2o6/g1w\nr8+jTNT9yu7VbPtS/Z/me4FnZ+aPqzJ/09vAAPfa3/Q2EBGrgb/MzPNG6/e8XbesuUj8mHtK1YV0\nc0R8OSJap1vRKIqIBZT/J9z8+34Y+Bn+vreV51ZdSjdExLkRMbvbFRrnZlFaMR8Af9PbWL973cTf\n9CiJiJ6IeA1lntgrRvP3vF2HNVwkfixdCbwBOA44BVgA/CgiduxmpbZz8yn/+Pr7Hhv/BZwIPB/4\nP8BzgO9WrfUapuq+fQL4cWY2nm/1N70NDHCvwd/0qIiIP4iI3wGPAOcCr8jMGxnF33MtJsXV+JeZ\nzctp/CoirqJMUvynwHndqZU0elq6LK6LiGuBm4HnApd3pVLj27nAU4Gju12RCaDtvfY3PWpuAA4B\nZgKvBi6IiGeP5gW295a1+4FNlIcnm80DVo19dSaOzFwD3AQ4imvbWUVZcs3fdxdk5q2Uf2P8jQ9T\nRPwz8EfAczPzt00f+ZseZYPc6y34mx6ZzHw8M2/JzKsz8wOUgYzvZhR/z9t1WMvMx4DGIvFAv0Xi\nR2W9LrUXETtR/gs/6D8OGrnqH9ZV9P9970IZ/eXvexuLiD2AOfgbH5YqPLwMeF5m3t78mb/p0TXY\nvR5gf3/To6MHmDqav+eJ0A066CLxGh0RcSbwbUrX5xOBDwOPAUu7Wa/xrnrmb1/K/zsD2CciDgEe\nyMw7KM+h/E1E/AZYCXwEuBP4VheqO64Ndq+r7TTg3yj/+O4LfIzSenzxlmdTOxFxLmVqiJcC6yKi\n0eKwJjM3Vn/7mx4FQ93r6vfub3orRcTfU579ux3YGXgd5dm/Y6tdRuf3nJnb/UaZN2klsAH4KfD0\nbtdpe9sooezO6h7fDnwVWNDteo33rfov/WZKd37z9sWmfT4E3A2sp/wju2+36z0et8HuNTAN+B7l\nf9Q2ArcAnwHmdrve42kb4P5uAk5s2c/f9Da+1/6mR+0+/0t17zZU9/IS4Pkt+2z173m7n2dNkiRp\nPNuun1mTJEka7wxrkiRJNWZYkyRJqjHDmiRJUo0Z1iRJkmrMsCZJklRjhjVJkqQaM6xJkiTVmGFN\n0pAi4vKIOLvb9WgVEZsj4qU1qMcFEfH+btdjtETE0oh4b7frIakwrEnqxCuAv228iYhbI+LPx+ri\nEXFaRFzd5qP5lHX5uqZaP/RFwD8N45inRsQ3qvu4udN7GREHR8SPImJDRNwWEX/VZp/nRsSyiNgY\nETdFxElt9vmTiFhRneeXEfGill0+CnwgInbu9DtJ2nYMa5KGlJkPZea60T5vREweTjW2KMi8NzMf\nG8UqjcQ7ga9n5oZhHDMDuBl4H/DbTg6ogtPFwK3AQuCvgA9FxJub9tkb+E/gMuAQSoD8l4h4YdM+\nR1HW7v0CcChlQen/iIinNvbJzOuq+r1+GN9J0jZiWJM0pOZu0Ii4HNgLOKdqFdrUtN8zq5af9VXL\nzz9FxIymz2+NiL+JiC9FxBrgc1X5P0TEjRGxLiJujojTI2JS9dlJwGnAIY3rRcSJ1Wf9ukEj4g8i\n4rLq+vdHxOciYsemz8+LiG9GxF9ExN3VPv/cuFa1zzuqFqkNEbEqIi4c5L70AK8Gvt1Utn/1PV7T\nVPanVZ0OAMjMX2Tm+zLzQuDRDv9jeD0wGXhTZq6ojv0k0Nxd+Xbglsz8P5l5Y2Z+GvgGsKRpnz8H\n/iszz672+SCwnBI6m30beA2Sus6wJmm4XgncSekWnQ/sBhART6Z0SX4d+APgBOBo4FMtx/8FcA2l\nVecjVdnDwInAgZQw8Wb6AsbXgLOA64B51fW+1lqpKhReDKwGFlFC1AvaXP95wD7Ac6trvqHaiIin\nU1qj/gbYDzgO+NEg9+JgYBfgF42CzLwR+EvgMxGxR0TsAXwG+KvMvGGQcw3lCOBHmfl4U9nFwP4R\nMbNpn0tbjrsYOLLp/ZEd7ANwFXD4MFs/JW0DO3S7ApLGl8x8sGpNW5uZ9zZ99H7gy5nZCEe3RMR7\ngB9ExNszs9GCdFlmntNyzr9vent7RJxFCXv/mJkbI2It8Hhm3jdI1V4HTAVOzMyNwIqIeCfw7Yh4\nX9OxDwDvzMwEboqI7wDHAP8K7AmsBb5TdfveAfxykGvuBWxqrVdmfqZ6DuwrlJazn1WtXFtjPnBL\nS9k9TZ+tqV7vabPPLhExNTMfGWSf+S1ldwNTqvI7tq7qkraGYU3SaDkEeFpEND/nFNXrAuDG6u9l\nrQdGxAnAu4AnAztR/m1aM8zrHwD8sgpqDT+h9CDsDzQC1XVVUGv4LaUlEOC/gduAWyPie8D3gG8O\n8jzadOCRAT57E3ATsAk4aJjfZThi6F1GZEN17hlD7Shp27IbVNJo2YnyDNrBlOB2SPX3fpSH1Rv6\nDVSIiCOAL1MejH8xpXv07yitOttC64CEpPq3MDPXUh7efw2lZenDwC8jYpcBznU/MCMi2v0f30OB\nHattt1Go9ypKN3CzeZT6rxpin4erVrXB9lnVUja7OvdgrZmSxoBhTdJIPApMailbDjw1M2/NzFta\ntsfbnKPhKGBlZv5DZi7PzJuBvTu4XqsVlEEI05vKnklp2bqx/SFbyszNmfn9zHw/JXDuDTx/gN2v\nqV6f2lwYEbsC51GmwDgf+GpETO20DgP4KfDs5sEQwLHAjZm5pmmfY1qOO7YqZ5B9XtiyD5TWxjsz\n84GtqrWkrWZYkzQSKynBYfeImFOVfQw4KiI+FRGHRMS+EfGyiGh9wL/Vr4EnRcQJEbFPNefYy9tc\nb0F13jkR0a7V7SvARuBLEXFQRDyPMlrygiGedfu9iHhxRLyrus6TgJMoXYFtw15m3g9cTQmFzT5H\n6U79KGVARQ9lkETjOpOraxxKaUF8YvX+yYNU76uU0PrFKPO0nUAZjHFW0z6fBfaJiI9Vo1LfQRlo\n0Tyh8T8Bx0fEe6t9PkQZkPHPLdd7FnDJIPWRNEYMa5I60TrH2QcpLU43A/cCZOa1wHOAp1BGUC4H\nPgTcNch5yMxvA+dQRm1eTRnReHrLbv9GeX7s8up6jSklfn++6rmy4yjdd1cBF1KeQXtX51+Thyij\nXS8DrgfeCrwmM1cMcsy/0DQfWUT8GXA88GdVK9164M+AN0fEcdVuu1ffdRnlAf6/pNyvLzSd5w0R\nsbnp+z1MaSXbmzL69EzgQ5n5r037rKR0Jb+A0uq3hDLVx6VN+/wUeG313a6pvu/LMvP6pmtPpQTm\nzw96tySNiej/nK0kaTgiYhpwA3BCZv5sFM/7IeDZmTlQF+w2ExGnAC/PzOPH+tqStuRoUEnaCtXU\nIicCvaN86uOBU0f5nJ16lOG1SErahmxZkyRJqjGfWZMkSaoxw5okSVKNGdYkSZJqzLAmSZJUY4Y1\nSZKkGjOsSZIk1ZhhTZIkqcYMa5IkSTVmWJMkSaoxw5okSVKN/f/FKF4S6ep5HAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10663da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the training set:\n",
      "Accuracy: 0.947867298578\n",
      "On the test set:\n",
      "Accuracy: 0.915\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y)\n",
    "print (\"On the training set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train accuracy is 94.8% while the test accuracy is 91.5%. This is the **baseline model** (you will observe the impact of regularization on this model). Run the following code to plot the decision boundary of your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zNxMVNomS8kzSDr1HUMIMfMKcn+QNxACL7oS7e/LPxHlk1FdS2FRP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\n6ZaV5JV9j0pjIGLu1YTPurzfmpADTbCQGTxTlrnQXTkVAPv7u4bdKyo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jmyTHLSMuYiH2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11447d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model without regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-regularized model is obviously overfitting the training set. It is fitting the noisy points! Lets now look at two techniques to reduce overfitting.\n",
    "\n",
    "---\n",
    "\n",
    "非正则化模型显然是过度拟合训练集，它拟合了噪音数据！现在让我们看看可以减少过度拟合的两种技术。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - L2 Regularization\n",
    "\n",
    "The standard way to avoid overfitting is called **L2 regularization**. It consists of appropriately modifying your cost function, from:\n",
    "$$J = -\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small  y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} \\tag{1}$$\n",
    "To:\n",
    "$$J_{regularized} = \\small \\underbrace{-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} }_\\text{cross-entropy cost} + \\underbrace{\\frac{1}{m} \\frac{\\lambda}{2} \\sum\\limits_l\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2} }_\\text{L2 regularization cost} \\tag{2}$$\n",
    "\n",
    "Let's modify your cost and observe the consequences.\n",
    "\n",
    "**Exercise**: Implement `compute_cost_with_regularization()` which computes the cost given by formula (2). To calculate $\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2}$  , use :\n",
    "```python\n",
    "np.sum(np.square(Wl))\n",
    "```\n",
    "Note that you have to do this for $W^{[1]}$, $W^{[2]}$ and $W^{[3]}$, then sum the three terms and multiply by $ \\frac{1}{m} \\frac{\\lambda}{2} $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost_with_regularization\n",
    "\n",
    "def compute_cost_with_regularization(A3, Y, parameters, lambd):\n",
    "    \"\"\"\n",
    "    Implement the cost function with L2 regularization. See formula (2) above.\n",
    "    \n",
    "    Arguments:\n",
    "    A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)\n",
    "    Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
    "    parameters -- python dictionary containing parameters of the model\n",
    "    \n",
    "    Returns:\n",
    "    cost - value of the regularized loss function (formula (2))\n",
    "    \"\"\"\n",
    "    m = Y.shape[1]\n",
    "    W1 = parameters[\"W1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    \n",
    "    cross_entropy_cost = compute_cost(A3, Y) # This gives you the cross-entropy part of the cost\n",
    "    \n",
    "    ### START CODE HERE ### (approx. 1 line)\n",
    "    L2_regularization_cost = 1/m * lambd/2 * (np.sum(np.square(W1))+np.sum(np.square(W2))+np.sum(np.square(W3)))\n",
    "    ### END CODER HERE ###\n",
    "    \n",
    "    cost = cross_entropy_cost + L2_regularization_cost\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 1.78648594516\n"
     ]
    }
   ],
   "source": [
    "A3, Y_assess, parameters = compute_cost_with_regularization_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost_with_regularization(A3, Y_assess, parameters, lambd = 0.1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **cost**\n",
    "    </td>\n",
    "        <td>\n",
    "    1.78648594516\n",
    "    </td>\n",
    "    \n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost. \n",
    "\n",
    "**Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\\frac{d}{dW} ( \\frac{1}{2}\\frac{\\lambda}{m}  W^2) = \\frac{\\lambda}{m} W$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation_with_regularization\n",
    "\n",
    "def backward_propagation_with_regularization(X, Y, cache, lambd):\n",
    "    \"\"\"\n",
    "    Implements the backward propagation of our baseline model to which we added an L2 regularization.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input dataset, of shape (input size, number of examples)\n",
    "    Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
    "    cache -- cache output from forward_propagation()\n",
    "    lambd -- regularization hyperparameter, scalar\n",
    "    \n",
    "    Returns:\n",
    "    gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    \n",
    "    ### START CODE HERE ### (approx. 1 line)\n",
    "    dW3 = 1./m * np.dot(dZ3, A2.T) +  lambd/m * W3\n",
    "    ### END CODE HERE ###\n",
    "    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
    "    \n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    ### START CODE HERE ### (approx. 1 line)\n",
    "    dW2 = 1./m * np.dot(dZ2, A1.T) + lambd/m * W2\n",
    "    ### END CODE HERE ###\n",
    "    db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    ### START CODE HERE ### (approx. 1 line)\n",
    "    dW1 = 1./m * np.dot(dZ1, X.T) + lambd/m * W1\n",
    "    ### END CODE HERE ###\n",
    "    db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[-0.25604646  0.12298827 -0.28297129]\n",
      " [-0.17706303  0.34536094 -0.4410571 ]]\n",
      "dW2 = [[ 0.79276486  0.85133918]\n",
      " [-0.0957219  -0.01720463]\n",
      " [-0.13100772 -0.03750433]]\n",
      "dW3 = [[-1.77691347 -0.11832879 -0.09397446]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess, cache = backward_propagation_with_regularization_test_case()\n",
    "\n",
    "grads = backward_propagation_with_regularization(X_assess, Y_assess, cache, lambd = 0.7)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"dW3 = \"+ str(grads[\"dW3\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **dW1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.25604646  0.12298827 -0.28297129]\n",
    " [-0.17706303  0.34536094 -0.4410571 ]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **dW2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.79276486  0.85133918]\n",
    " [-0.0957219  -0.01720463]\n",
    " [-0.13100772 -0.03750433]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **dW3**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-1.77691347 -0.11832879 -0.09397446]]\n",
    "    </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now run the model with L2 regularization $(\\lambda = 0.7)$. The `model()` function will call: \n",
    "- `compute_cost_with_regularization` instead of `compute_cost`\n",
    "- `backward_propagation_with_regularization` instead of `backward_propagation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6974484493131264\n",
      "Cost after iteration 10000: 0.26849188732822393\n",
      "Cost after iteration 20000: 0.2680916337127301\n"
     ]
    },
    {
     "data": {
      "image/png": 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CDj0UXvIS+NBOV8RJkiRNzKpVq1i0aBHAosxcNZF1VX7NWmY+CqwEjmu0RUSU3189wmL/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\nm4BrgSXAiog4NDM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ezPzFhKqWNGGGNUnjcQdFcNg3IvYs2z4IHBURF0TE4RHxuIh4cUS0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\nJEmSauz/A55Ynvdyv0saAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.938388625592\n",
      "On the test set:\n",
      "Accuracy: 0.93\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, lambd = 0.7)\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats, the test set accuracy increased to 93%. You have saved the French football team!\n",
    "\n",
    "You are not overfitting the training data anymore. Let's plot the decision boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lWw3M6mK3y4DtwE+UUrlKqSNKqT8opax9HrAQQgjRz24cZQzO7EkrnhhaBtu3HwaYgSKn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\nUp9mlvGBPjCSObc8Q2t5Hi57I4bgOExhyccd5+5spbl4Oz5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jH9FFpVBU9RU19hJiFl49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YUbLc+1lWNDz/UUoulfzp4pI7lFrufljmquSxj9LLeUkluqi2lxubmG2aQSzjbaebmrHJ1QcWNS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\nHx84Rn3I/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cXkDNqPmSzCScLErr6uSSHyzrQm82p7DXe5t7BExqNHzTqa8IoA18TmcaEt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\n4TVC/w+OZPHA3w/18jH2V4cZY7sffgwBkoHWSMSowkuTPDWcP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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf3b6128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with L2-regularization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Observations**:\n",
    "- The value of $\\lambda$ is a hyperparameter that you can tune using a dev set.\n",
    "- L2 regularization makes your decision boundary smoother. If $\\lambda$ is too large, it is also possible to \"oversmooth\", resulting in a model with high bias.\n",
    "\n",
    "**What is L2-regularization actually doing?**:\n",
    "\n",
    "L2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. It becomes too costly for the cost to have large weights! This leads to a smoother model in which the output changes more slowly as the input changes. \n",
    "\n",
    "<font color='blue'>\n",
    "**What you should remember** -- the implications of L2-regularization on:\n",
    "- The cost computation:\n",
    "    - A regularization term is added to the cost\n",
    "- The backpropagation function:\n",
    "    - There are extra terms in the gradients with respect to weight matrices\n",
    "- Weights end up smaller (\"weight decay\"): \n",
    "    - Weights are pushed to smaller values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Dropout\n",
    "\n",
    "Finally, **dropout** is a widely used regularization technique that is specific to deep learning. \n",
    "**It randomly shuts down some neurons in each iteration.** Watch these two videos to see what this means!\n",
    "\n",
    "<!--\n",
    "To understand drop-out, consider this conversation with a friend:\n",
    "- Friend: \"Why do you need all these neurons to train your network and classify images?\". \n",
    "- You: \"Because each neuron contains a weight and can learn specific features/details/shape of an image. The more neurons I have, the more featurse my model learns!\"\n",
    "- Friend: \"I see, but are you sure that your neurons are learning different features and not all the same features?\"\n",
    "- You: \"Good point... Neurons in the same layer actually don't talk to each other. It should be definitly possible that they learn the same image features/shapes/forms/details... which would be redundant. There should be a solution.\"\n",
    "!--> \n",
    "\n",
    "\n",
    "<center>\n",
    "<video width=\"620\" height=\"440\" src=\"images/dropout1_kiank.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "<br>\n",
    "<caption><center> <u> Figure 2 </u>: Drop-out on the second hidden layer. <br> At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\\_prob$ or keep it with probability $keep\\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration. </center></caption>\n",
    "\n",
    "<center>\n",
    "<video width=\"620\" height=\"440\" src=\"images/dropout2_kiank.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "\n",
    "<caption><center> <u> Figure 3 </u>: Drop-out on the first and third hidden layers. <br> $1^{st}$ layer: we shut down on average 40% of the neurons.  $3^{rd}$ layer: we shut down on average 20% of the neurons. </center></caption>\n",
    "\n",
    "\n",
    "When you shut some neurons down, you actually modify your model. The idea behind drop-out is that at each iteration, you train a different model that uses only a subset of your neurons. With dropout, your neurons thus become less sensitive to the activation of one other specific neuron, because that other neuron might be shut down at any time. \n",
    "\n",
    "### 3.1 - Forward propagation with dropout\n",
    "\n",
    "**Exercise**: Implement the forward propagation with dropout. You are using a 3 layer neural network, and will add dropout to the first and second hidden layers. We will not apply dropout to the input layer or output layer. \n",
    "\n",
    "**Instructions**:\n",
    "You would like to shut down some neurons in the first and second layers. To do that, you are going to carry out 4 Steps:\n",
    "1. In lecture, we dicussed creating a variable $d^{[1]}$ with the same shape as $a^{[1]}$ using `np.random.rand()` to randomly get numbers between 0 and 1. Here, you will use a vectorized implementation, so create a random matrix $D^{[1]} = [d^{[1](1)} d^{[1](2)} ... d^{[1](m)}] $ of the same dimension as $A^{[1]}$.\n",
    "2. Set each entry of $D^{[1]}$ to be 0 with probability (`1-keep_prob`) or 1 with probability (`keep_prob`), by thresholding values in $D^{[1]}$ appropriately. Hint: to set all the entries of a matrix X to 0 (if entry is less than 0.5) or 1 (if entry is more than 0.5) you would do: `X = (X < 0.5)`. Note that 0 and 1 are respectively equivalent to False and True.\n",
    "3. Set $A^{[1]}$ to $A^{[1]} * D^{[1]}$. (You are shutting down some neurons). You can think of $D^{[1]}$ as a mask, so that when it is multiplied with another matrix, it shuts down some of the values.\n",
    "4. Divide $A^{[1]}$ by `keep_prob`. By doing this you are assuring that the result of the cost will still have the same expected value as without drop-out. (This technique is also called inverted dropout.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation_with_dropout\n",
    "\n",
    "def forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):\n",
    "    \"\"\"\n",
    "    Implements the forward propagation: LINEAR -> RELU + DROPOUT -> LINEAR -> RELU + DROPOUT -> LINEAR -> SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input dataset, of shape (2, number of examples)\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n",
    "                    W1 -- weight matrix of shape (20, 2)\n",
    "                    b1 -- bias vector of shape (20, 1)\n",
    "                    W2 -- weight matrix of shape (3, 20)\n",
    "                    b2 -- bias vector of shape (3, 1)\n",
    "                    W3 -- weight matrix of shape (1, 3)\n",
    "                    b3 -- bias vector of shape (1, 1)\n",
    "    keep_prob - probability of keeping a neuron active during drop-out, scalar\n",
    "    \n",
    "    Returns:\n",
    "    A3 -- last activation value, output of the forward propagation, of shape (1,1)\n",
    "    cache -- tuple, information stored for computing the backward propagation\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(1)\n",
    "    \n",
    "    # retrieve parameters\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    Z1 = np.dot(W1, X) + b1\n",
    "    A1 = relu(Z1)\n",
    "    ### START CODE HERE ### (approx. 4 lines)         # Steps 1-4 below correspond to the Steps 1-4 described above. \n",
    "    D1 = np.random.rand(A1.shape[0], A1.shape[1])     # Step 1: initialize matrix D1 = np.random.rand(..., ...)\n",
    "    D1 = D1 < keep_prob                               # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)\n",
    "    A1 = np.multiply(D1, A1)                          # Step 3: shut down some neurons of A1\n",
    "    A1 = A1 / keep_prob                               # Step 4: scale the value of neurons that haven't been shut down\n",
    "    ### END CODE HERE ###\n",
    "    Z2 = np.dot(W2, A1) + b2\n",
    "    A2 = relu(Z2)\n",
    "    ### START CODE HERE ### (approx. 4 lines)\n",
    "    D2 = np.random.rand(A2.shape[0], A2.shape[1])     # Step 1: initialize matrix D2 = np.random.rand(..., ...)\n",
    "    D2 = D2 < keep_prob                               # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold)\n",
    "    A2 = np.multiply(D2, A2)                          # Step 3: shut down some neurons of A2\n",
    "    A2 = A2 / keep_prob                               # Step 4: scale the value of neurons that haven't been shut down\n",
    "    ### END CODE HERE ###\n",
    "    Z3 = np.dot(W3, A2) + b3\n",
    "    A3 = sigmoid(Z3)\n",
    "    \n",
    "    cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n",
    "    \n",
    "    return A3, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A3 = [[ 0.36974721  0.00305176  0.04565099  0.49683389  0.36974721]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_with_dropout_test_case()\n",
    "\n",
    "A3, cache = forward_propagation_with_dropout(X_assess, parameters, keep_prob = 0.7)\n",
    "print (\"A3 = \" + str(A3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **A3**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.36974721  0.00305176  0.04565099  0.49683389  0.36974721]]\n",
    "    </td>\n",
    "    \n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Backward propagation with dropout\n",
    "\n",
    "**Exercise**: Implement the backward propagation with dropout. As before, you are training a 3 layer network. Add dropout to the first and second hidden layers, using the masks $D^{[1]}$ and $D^{[2]}$ stored in the cache. \n",
    "\n",
    "**Instruction**:\n",
    "Backpropagation with dropout is actually quite easy. You will have to carry out 2 Steps:\n",
    "1. You had previously shut down some neurons during forward propagation, by applying a mask $D^{[1]}$ to `A1`. In backpropagation, you will have to shut down the same neurons, by reapplying the same mask $D^{[1]}$ to `dA1`. \n",
    "2. During forward propagation, you had divided `A1` by `keep_prob`. In backpropagation, you'll therefore have to divide `dA1` by `keep_prob` again (the calculus interpretation is that if $A^{[1]}$ is scaled by `keep_prob`, then its derivative $dA^{[1]}$ is also scaled by the same `keep_prob`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation_with_dropout\n",
    "\n",
    "def backward_propagation_with_dropout(X, Y, cache, keep_prob):\n",
    "    \"\"\"\n",
    "    Implements the backward propagation of our baseline model to which we added dropout.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input dataset, of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector, of shape (output size, number of examples)\n",
    "    cache -- cache output from forward_propagation_with_dropout()\n",
    "    keep_prob - probability of keeping a neuron active during drop-out, scalar\n",
    "    \n",
    "    Returns:\n",
    "    gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n",
    "    \n",
    "    dZ3 = A3 - Y\n",
    "    dW3 = 1./m * np.dot(dZ3, A2.T)\n",
    "    db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n",
    "    dA2 = np.dot(W3.T, dZ3)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dA2 = np.multiply(dA2, D2)      # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation\n",
    "    dA2 = dA2 / keep_prob           # Step 2: Scale the value of neurons that haven't been shut down\n",
    "    ### END CODE HERE ###\n",
    "    dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n",
    "    dW2 = 1./m * np.dot(dZ2, A1.T)\n",
    "    db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "    \n",
    "    dA1 = np.dot(W2.T, dZ2)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dA1 = np.multiply(dA1, D1)      # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation\n",
    "    dA1 = dA1 / keep_prob           # Step 2: Scale the value of neurons that haven't been shut down\n",
    "    ### END CODE HERE ###\n",
    "    dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n",
    "    dW1 = 1./m * np.dot(dZ1, X.T)\n",
    "    db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "    \n",
    "    gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n",
    "                 \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n",
    "                 \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dA1 = [[ 0.36544439  0.         -0.00188233  0.         -0.17408748]\n",
      " [ 0.65515713  0.         -0.00337459  0.         -0.        ]]\n",
      "dA2 = [[ 0.58180856  0.         -0.00299679  0.         -0.27715731]\n",
      " [ 0.          0.53159854 -0.          0.53159854 -0.34089673]\n",
      " [ 0.          0.         -0.00292733  0.         -0.        ]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess, cache = backward_propagation_with_dropout_test_case()\n",
    "\n",
    "gradients = backward_propagation_with_dropout(X_assess, Y_assess, cache, keep_prob = 0.8)\n",
    "\n",
    "print (\"dA1 = \" + str(gradients[\"dA1\"]))\n",
    "print (\"dA2 = \" + str(gradients[\"dA2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **dA1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.36544439  0.         -0.00188233  0.         -0.17408748]\n",
    " [ 0.65515713  0.         -0.00337459  0.         -0.        ]]\n",
    "    </td>\n",
    "    \n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **dA2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.58180856  0.         -0.00299679  0.         -0.27715731]\n",
    " [ 0.          0.53159854 -0.          0.53159854 -0.34089673]\n",
    " [ 0.          0.         -0.00292733  0.         -0.        ]]\n",
    "    </td>\n",
    "    \n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now run the model with dropout (`keep_prob = 0.86`). It means at every iteration you shut down each neurons of layer 1 and 2 with 24% probability. The function `model()` will now call:\n",
    "- `forward_propagation_with_dropout` instead of `forward_propagation`.\n",
    "- `backward_propagation_with_dropout` instead of `backward_propagation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6543912405149825\n",
      "Cost after iteration 10000: 0.0610169865749056\n",
      "Cost after iteration 20000: 0.060582435798513114\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJEk1NinD2uzZhjVJkjQ+TMqw1t3t1B2SJGl8mLRh7b77ILPqSiRJkgY2acPak0/CihVV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\nwwY41hvwOz08mbk6M2/LzGs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1259e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.928909952607\n",
      "On the test set:\n",
      "Accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, keep_prob = 0.86, learning_rate = 0.3)\n",
    "\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropout works great! The test accuracy has increased again (to 95%)! Your model is not overfitting the training set and does a great job on the test set. The French football team will be forever grateful to you! \n",
    "\n",
    "Run the code below to plot the decision boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I0pxhiyM0+XyCk+bw8fY/ER0+FZvVm1PFe7F4BxA//6uDalspxYQV3yL9jYd5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\nqPctzwaotEzLup4PvribxsKtw2rK21+EEOz98HFcTTWcPe1nWINTqK7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PWjzo5wvLPIsD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+BsIBuwA+8CN0kpG8YlaIUxQxcZXO5p76rBaDhY/dfRVQ1ARKRtQuIaDjmrrsSSPJ3Gkrep7+nA\nlHIyuQu/jCluaEa5E4WUktI37sFqSuS0JT9FrzPj9br4aOP9lL7+Zxb/4HFUYzQj43XaKXnqRlyd\n9STFFuLx2dn96p207VtJ/nnXI4Qq5DYow6XN5yabgQ8iIQTJ0kS7Nzxbc620JHJF/DQea97Pa1QC\noEXwk6TCkFQGA8wz2cjSRfKoZzfflNNJx8w2WnmFCr5gTcGiOXoumcKxydabyR5ideyXYtJ5oa2S\nu/ybOUtmoEfN+9QGhXxcaIT8aInt8+2spZtMDnpo1tCNAGxj7Os51ZhUIg94GkgAVgE64HHgn8C3\njrG/EZgD3AZsA6KBvwGvAOPTmV1hzIhMyMGcOI0N258gQmskNjqX5va9FO94CktKQdgLJQg++ONn\nnEL8jFMmOpRh0d1cjrO9lqUn3YBeF3zAaLUG5s/4Bq99eAudVduIyZ4/JueuWvssXkcb55/yB6LM\nQY+/itq1fLLp7yz+zgJ+850ZEybuDpBniGKzpw2vDKDtm110Sh+7aed8Y3jkPR2OEILL46dzXnQ6\n6xzNqIRgqTme6BA+VFVC8KfMhdxcvYl7erf2bz/VkshPkguHNdZuZyf/at7Lpu42DCo1Z1iT+W5C\nHhZlufeYxGgiuD9rCX+p38mjzt0AZEeY+VPiwlEbTYfq+1gQGUuS1sBj3j1cIQtII5KdtPMy5Sw3\nJ2Abo+44U5VJI/KEEPnAmcB8KeWWvm3XAG8IIa6XUjYefoyUsqvvmEPH+RGwXgiRKqWsHYfQFcYI\nIQQzLvgFO57/Nas/uR0hVEgZwBSbScH5N050eFOaQJ/B9AET6QNE6MbeVLqt9HNy05b1CzyAzJQl\n7Cx7Ddsna8hv2TFh4u4AF8dm8769gbvZwukyDT8B3qIaVPCVIbYzmyjitHrOG8ME/CSdkUdylrGn\n106Lt5fsCPOw7Vn2uOz8oGIt8dLAhWTjCHhY3V7H1p4OHspZqrT3Og5ZejP3ZS+h0+fBKwPEaiJG\nbWcUyu9DI1TclbGQ6yuL+Y2vGBWCAJJCg5Ubp2CHl7Fm0og84CSg44DA6+NdQAKLCc7ODQVr3zGd\noQ1PYSLQRyUw//IH6KgswdXZgDEmBWtG0Zh3UZgKyICfnrYaVCo1hpjUYd3oIxOy0USYKK14nyVF\n/9d/7N7K9xEqDVGpR5+V8TrttFdsAgnRWfPQmYaf0B8I+FCpBs4OCCFQSTUNH7dRUnH0pPPxZJrB\nwl8yF3Fvwy7+7g52WSk0WPl18hKSxqhTxWRCCEGBwUrBCB1lHm3aS6w08CsWoBVBAbFIJnCbu5j3\n7Q2cHZ0awminJtYQLo2P5vto9LjY1NNKhFBzkjkOk1pLtt7Mc3mnsM7RQrPXRbbeQpExOmy8NScT\nk0nkJQLNh26QUvqFEO197w2KECICuBN4WkrZHfoQFSYCoVKP2dLgVKV17+eUvfsQvY4WAEy2NKad\ndc0xxdnhqLV6Mpd/i33v/pNuVwtJsYW0tO+npnET6SdddFTxVrvxFco/eBQZ8AEgVBqyVl5G2qIv\nDyv2mOyFlO37jBm5Z2OICObsNLbuptVexdLU8Ok7PC/SxuO5y2j1uVGBsswUQrb0tHMW6f2CAiBD\nmMmQkWzpaQuZyAtISaffg0GlVlqrHYeRfB9SSh5o2sOzreUcMIsyCDU3pczmdGsyGqFimSVhnD7B\n1GXCr1ohxB3A8dbWJFAQgvNogOf7xvvBaMdTUJis2Gt3sfPlO0hNKGLGrCvwBbxs3/sq25+7lfmX\n34/BOrRetinzz0dnij5oKh2VyPSzryVx1hlH7NtRtZWy9x4iP+sMZuddAAK2732V3R88gik2Y1gi\nPePki2kvK+aV928iM3kRHm8PNQ0bmWuK5ZSoIf3eGzeEEMQp4i7kGFVq7P6BBSwBKenCizFE9hrv\ndNbxcNNe6r1O1AhOjUrkuqTCkOYoThVG8n283lHDM302Q6eRigsfz8sybqstIUdvJktptRYSJlzk\nAXcDjw2yTznQCAww4BJCqIGYvveOySECLw04baizeGXvPYxGP3BpJb5g5aRLkldQOJTaDS8RZU7i\nlEXXoupb1k6w5fHimp9Sv/kNck777pDHistfTlz+8kH3a9jyJtaoNBbO+lb/ksuCwktoaiulvuTN\nYYk8fVQ833zqNnpXv8rmt7di8Xr4fvw0vhKTOcDIV2F8KXXZea61gvJeB4k6A1+2ZbJwjKxizopO\n5fnWShbKeKYLK34Z4DUq6cDNmdaUUY//nr2e22pLmEccF5JNK72stldxbe96Hs1dFtLrrNXby/Nt\nlWzsbsWo0nCGNZkvRqdOqmt5JN/H/9qrmUNsv82QAQ1XyAJ2085rHTX8OAQdVsKZNZ11rLHXD9jW\n4/eF/DwTLvKklG1A22D7CSHWAlYhxNxD8vJWAQJYf5zjDgi8bOBUKeWxm/odRs6qKzEn5g51dwWF\nSUFPSxVZcbP7BR6AVqMnISaP7taqMTmnu6uFWEv6gJwaIQQxURk0ddUNeZyDfWYbcEWpKTEvAuUH\n/4TzuaOJm6o2YUNPPtFUubu4zrGe65Jm8DVbVsjPd1lcLiU97dzp2kyyNNKDDzseroifToFhdMbN\nUkoea9rHbGz8kJn91+x0aeV290Y+6Wri1KihzXYPRqPHyVVln+P0+5lDLA683OXczlpHM79Pnz/u\nvWR9MsAnXU2U9TqI0+pZFZVE5BCqY0fyfTR7XaxgoDWPRqhIlZG0jGHhVrhwhjWFMw4TwKUuO5eX\nfRrS80y4yBsqUso9Qoi3gYeFEFcTtFC5D3jm0MpaIcQe4EYp5St9Au9FgjYq5wJaIcSBRf52KaV3\nfD+FgsLEE2GJpdVeOWBbQAZo76rGHD88E2N3VysNW9/C2VaD3ppIUtFZGKKPfACa4rNo2Lsev9+D\nuq+vpj/go6FlJ5HZc4Z0rgPed7J4Da7nN1MyRi3JDqXd56bR4yJJZ1CW6Y6BX0rurtvJDGK4hllo\nhAopJU+xlwcb93BmVOj974xqDQ9kL+GTriY29bRiUGk4Iyo5JCa5HhmgwtPNFaQN+FGSJSzESj17\nXPaQibxHmvfi80t+x2KsInh9bZLNPODYwbruFpaax697TIu3l+sq1lPp6caKji68PNi4mz9mLGCO\n6fieoyP5PnL1Frb3tHGezOwXs93SSxl2lurjQvrZTmQmjcjr4xKCZsjvEjRDfgG49rB9pkG/C2kK\nQXEHUNL3X0EwL+9U4OOxDFZBIRxJnncuu17+A5t3Pc+M3LPw+72U7H6RHmcreXPOHvI49tpdbH/u\nV6gQ2KKyaKooobb4ZQovvBlb7kAbypT559O04z3WrL2bWdPOAQQ797+Js7eT/IVfCvEnHD1Ov4+7\n67ezxt5AAIkawZnWZH6WPAu9Ys8xgPJeB00+F5eS37/EKITgHJnJ+7KO4p5WVkUlh/y8GqHi1Kik\nkAmuA2iFCqPQ0CidA7Y7pRc7HqJDKFg/6WpmJSn9Ag9gHnEkYeTTrqZxFXl/rNtGl8fLrSwgU1jo\nkG4eDuzk5urNvJR32qA2KMP9Pi6Jy+YnPRv4Ozs4TabixMfrVKBTqTnvGG3ZFIbPpBJ5UspOjm18\nfGAf9SH/XwUod2QFhUOIyzuZzGXfYufnz7Bj32sAqDQR5J19LeakaUMaQ8oApW/cQ4w5jVVLfoZO\na8Tn9/BR8f2UvvlXlvzg36g0B5d5THEZzPzabex/+wHeW/dnAAzWZGZ+9ddExmcPer4BHSzGYRbv\n97VbWedo4SJymYaVUjr5X2c53oDkN+njU8Hr8Hv5T8t+3u1qxCslJ5lsXBaXO2xPubHm4GSXHLBd\n9r0WTC7bC5UQnBOTyqttNeTIKIqw4cDLE5SCgDNCKFiDMw7yiO1Hbhlb2ry9rO1u4TvkkymCFevR\nIoLLZD6/8K/jc0dzyMX0osg4bkudy/2Nu/mTL5iBlae3cG/KYmKVYqWQMalEnoKCQmjIOPlikorO\npKOqBKHSEJM9H80wxEN3UzmuznqWLb0JnTZYnKRR65g34+u89sEv6azeSkz2ggHHRGcUseDKf+Jq\nr0VKidGWOqif4US0J6t19/Cho5HvkM9yEXygZ2BGK1U82VXK9z35JOpGaPA2RHoDfn5YsZ4aby9Z\n6cvRavR8VP0pn1as5ZHspSSHkddedoSZZK2BN71VTJPW/uXa16kkQqjGrPhiLLkqIY+KXgd/69mG\nEQ1u/GiE4La0uSG1wllhSeCTzgZOlSnEiOC4xTTTiJMV42gf0uUPZi7FM/C6jkWPgCMqZ0PF6dZk\nTolKpNrdg06oSNEZFS+8EKOIPAWFExRdZAwJhaeN6NiAL3jTPyDw+sfse33g/cMRQmC0Db4UM5G9\nZ6vcweL7mQzMQ5pFDLLv/bEWeas7aynv7eLcU24nOirYfWJG7tm88f4veKJlf1g5/6uE4PrkWdxY\nVcwvWEuejKYSB/X0cH3STMyTsM2YQaXhr5mL2epsZ5uzA4tay6mWJKJCnFt4RcJ0NnS3cotvPbNl\ncMZwNx2ssiSxKHL88tJSdEaiVFrWB5rJ42B7s2KakUChYXQtz46HRqiG3HtXYfgoIk9BQWHYRCbk\noNWb2VOxhqVzvtv/67u04l1Uau2QTZUP52D17KesPXUba4Hxvk0l9Am4SrqI5uCDtgIHwJgLPIDi\n7lYSbNP7BR6AXmcmPfUk1teEtvouFCw2x/FI7jKeb62k3O2gQGvhZtusQRP2wxkhBHNMtjH9DPFa\nA4/mLuOFtko2dbdhVqm5xVrEF6wp41pZq1OpuTQ+l/sad+OWPoqIpYZu1lDDcnMC0wyWcYtFIbQo\nIk9BQWHYqLURZK68jH1v34+jp5nkuEKa2/dT37yNzGXfQmscXpXjoeLu81lPD+vYDp+bHc4ODCoN\nc0wxo/YXy9VbmG2I5inXXrRSxXSs7KGTZ9jLPKONjIjIUY0/FCKECq+354jtHo+TiDD1T8vRW7gp\nNXxmGCcL0ZoIrkzI48oJbu7wDVsWEULNEy37Wetrwig0XBiTwVUJeRMbmMKoUESegoLCiEiec3Zf\nx4uX2FX1HnprIvnnXj8is/DLpg/fF0tKycPNe3m6pQxvX6p6rCaCX6fOZV7k6GZffps+j5uqNvKX\n3q392woNVn6TNjS7l9GyKiqZd6o3sr/qY3LSlyOEoKV9H1V1a7ksdvBCFQWF4SKE4EJbBl+KSac7\n4MOoUk8qQ2aFo6OIPAUFhRETO20JsdOWTMi5X+2o4d8t+zmfTFaQTBcenvPt5+dVxfx3+imjaicW\np9Xzr5yT2eHqoM7tJC3CxAyDddySwk82x/NFaxpvlvyL3fvfQKPR09JZQaExhovGwFx4MiGlZKuz\ng1KXHZsmguWWhEHtPRSGjkoILJMwj1Lh6CgiT0FBYcI4tMDCeddzw7JGeb61ggXEc4EIzmzFoOdH\nchY/k5/zZkctl8WPrluNEIJZxhhmGWMG3znECCH4ZcosVkUl8oG9Abd0c1JqEadZktGqTtzZFYff\ny41VG9nqbEeLCi8BrGodd2bMn5DvKdzpDfhp8fYSrdENqXOFwtRDEXkKCgrjzqE5eCOtnq33OlnM\nQO8uo9CSLI3Ue47MZ5tsCCFYYo5nyTga4oY7f63fyT5nF9dRxCxiaMHFI/7d3Fi5kZfyVylG1X34\nZIBHmvfyQmsVTulDi+BMawrXJhViVCuP/ROJE/cnoYKCwoRwUOCNrj1Zmi6SPQxsRd0lPdTSQ/o4\nFEcojC89fh/v2us5l0xmCxtCCOKFkSuYgT3g5eOuxsEHOUH4R+MenmwpZ6VM5ufM5Utk825nA7fV\nbBn8YIUphSLpFRQUJiUXx2Zxe91WnpJ7WdmXk/cS5ehVar4YnTrR4SmEmC6/Bx+SFAaadsehJwI1\nbT73BEUWXnT7vbzUXsW5ZPSnMhQQTYyM4KHuXZT3OhRfuhMIReQpKExx/N5eZCCAJmLiuyQcyMEb\n7SwewFnRqbT73TzWtI/3ZC0A6ToT96QuIloTMcjRCpONOK0eq1rHZn8Lsw4xquEF94QAACAASURB\nVN5JO278TNcrXm4A1e4e3DLAXAaaKR94va/Xroi8EwhF5CkoTFFcHfWUvfcwbeUbQQawJBeQferl\nRKXOGPdYxqqDxSWxOXwpOoO9vXaMKg3T9RalLVIICUhJnceJTqj6TaInCo1Q8e24HO5r3I2QMI84\n6unhdSqZaYhm3iQ2Xg4ltr4fOLV0k8FBMVdDsJNLrEbpC3sioYg8BYUpiMdpp+SpG9GhYdHMb6JR\nR1Ba9T7b/nszc799N5EJOeMSx3i0JzOpNcyd4Ae8X0pKXXa8MkC+IWpKWHp81NXI/Q27qPe6gKBP\n4M+TZ01o94Nv2LKQwJMtZXzor0eN4LSoJH6aVKiI+z4SdAaWRMbxYncZMVJPPlZq6eE/7CFVa5zU\nXUiOhjvg598t+3mzo5Yuv4dCQzTfiZ82aq/MqYIi8hQUpiANJavx93Zz1ul3Y9RbAchKXcIrH9xM\nzfoXKTj/hgmOcOqwsbuVO2q30egLiiGzSssPE/M5LyZ9kCNDh08G+MDewGeOZlQIVlgSWG5JRD1C\n4bO5u41bqjcxCxvfYDoufLzhquKainU8NW0FtlF4EI4GIQQXx2bz1ZhMmn29RKm1ijXIUfhlymx+\nXlXMn3q3oEHgQ5KoMfCnjIUjvibCDSklnzuauat+O20+N3lYWUEKW5wtXFu5nrszFrLYPH79f8MV\nReQpKExBHPWlJNjy+wUegFqtIyNpPmV1GycwsqlFrbuHG6qKyZFR/B8FRKBmTaCGO+u3E681jMtD\nxh3w8/OqYjb1tJGNBT8B3rbXscKcwO3p80bUteCp1jLSMHMNs/t7qBbIaG4IrOWVjhouj58W6o8x\nLLQqFSm6ic8xDVdsWj2P5Cxjc08b5W4HiVojJ5njpkwHi4CU/KFuK6s760jCSDYW9tCJlwA/pYj7\n2M4/m/YoIg9F5CkoTEk0BguOpmqklAOWsbp6mo7ZV9bX2w1CFZICjQPLtPNis3De8McxWaYNB15u\nr0In1VzDbCJEcIn2CllAI07+21o+Lg+ZV9qrKelp5+fMoUAEDYG3yBbuc2xnTWc9Z4+g0nifq4ul\nJPULPACz0JErLezv7QpZ7ApjhxCC+ZGxzI+MHdHxUkq6/F4iVOqw8x/8zNHM6s46rqCAk0XQK7NM\n2vkTW1hDLctI4pHe3fT4fZhOcF/AE/vTKyhMURJnn8HWHe9SsudFZk0/H5VKQ3n1p9Q0bGbaF64e\nsK+9dicV7/0Le+NeAGIy55Fz+lUYbcMXB4fn4H0eJuKuy+fhzc5a9vV2kaA1cE50Wkhmgqrc3WRj\n6Rd4EHy45stoNrmbRz3+UHjXXs8cYvsFHsBcEUe+tPKefWQiL1arp8bfPWCbTwaox0mB5ug/EhSm\nDh93NfJQYykVnm7UCE6xJHJt0owJW6Y/nPft9aQT2S/wAHJEFItkAhtoYhlJaBBop8jS9GiY+Luv\ngoJCyLGmzSRzxaVs//g/7C5/G5VKg8fTQ0LhqSQVndW/X3dTGdv/ezPpASNfpQAvAVZXlbLtyZ8z\n77sPojNFH/c8MuCncfu7tOz6kICnl1zvdNJuunzU9iihpLzXwY8r1uHwe8nEzCc08VRLGbelzeWU\nqKTBBzgOyToTa6jHKwNo+5bCpJSUYR+35URPIICVI2daDGhwBwIjGvPCmHTurN/OalnFaaTixs/z\n7MeOm/Ni0kYbskIY87mjmV9Wb6KQGL5PIR24eaurmmt61/NY7rKwKCpyywCGo8gXIxp68fMONZwa\nlYQuDGKdaMLjLqygoBByMk76BnF5y2jd+znS7yM6ez6WpOkD9qlZ/yLRAR03ybn9ImWejOPn7nXU\nl6wm8+RLjjm+DPjZ9dLvaS1bT6GwESk1PPXXt1jz7HreO28GEB5dJ+6s24bRr+VXLMQqIvBIPw+z\ni9/XbmVhZNyolnMuiEnn5fYq/sEOLpTZwZw8aiilkzti54fwUxybxeY4XnJX0SHdRIugfUaTdLKD\ndr5rnj7I0Ufn3Og0ynsdPN9exguUIQGdUPGL5CJyFT+6I/DJAGsdzezrdRCv1XOqJWnSLhM+3ryP\n6Vi5jqL+5fpCGcOtng18YG/grDAwGl8UGcvdXTuolg7SRdAmxiE9rKURB17SdSZ+lFgwwVGGB5Pz\nKlRQUBgSxpgU0pd87Zjvd9ftZrmM6Rd4ABaho0BGUVtfetyx2/ZvoLVsPdcwK2i0KqA90Mtvmzbx\nx7XlfIfZIfscI6XB42Snq5OrmYm1TwDphJqL5DR+Lj9nbXczp0clD3vcgJRUuoPLmbelzeWuuu3c\nGtgAQIRQ8aOEAlZYEkP3QY7D122ZvNNZx298GzhJJuJHspZGknQGvjTCCl8hBNcmF/LV2Cw2drei\nEypONsdj0ehCHP3kp9Xby3WV66lwd2NBiwMv9zfs5q6MBcw2xQw+QBghpWS3q5OLmT4gHzNVRJKC\niZ2uzrAQeWdZU3m5rZo73ZtZIhPRo+ZzGvGKAFfH5/M1W2ZYzDiGA4rIU1A4gdEarTQ6BvZ/lVLS\nqHIfs0DjAG3715OsMjNXHiwuiBF6lvoSeHVvI98ZHyu+4+IK+AGIZKDNxoHXLr9v2GNu6G7hz3U7\nqPU6AUjWGvlF6mx0Qo1HBphjisE8jrYeNq2eh3JO5j8t+/m0qwkVgvOi0rg0LnfUcaTojKSMoxXM\nZOSPddvpdHu5hQVkCwvtspeHA7v4ZfUmXsw7bVKJDSEEUWodTX7ngO1u6aed3rDpJKNXqbk/ewlP\ntJTxvr0BrwywMjKBS+NzSVaqrgegiDwFhROYhKIz2fr2fXxIHctJwofkdSppDnRTNPsLEx3eqEmP\nMGFTR/CRv458ae2vNP6IOgQwb5iVh+W9Dm6oLCYXKz9lGgLBW94qbqnezMM5JzPdMDFFCXFaPT9L\nnsnPkmdOyPlPVNp9btZ2N3MZ+WSL4DJ2jNBzqczjZv961jqaR533Od6cE53Gs60V5Mto5hKLEx/P\nsA83Ac6ypkx0eP1EqrVcnZjP1Yn5Ex1KWKOIPAWFE5jEoi/QVV/Kf7a/w7OinAASr/STteIyrGnH\nFwy23MXs3L6GLbQwVwRn89plL5/TwGmG8HiwaYSKqxPz+V3dVux4mC1tVOGgmGYuiE4fdnHEc20V\nWNBxHUX9S9x50sovWcdzbRXckjpnLD6GwhApddlZ01mHM+BnrimGUyxJaFVj5w1n93mQQAIDW77F\nY0AAnX7PmJ17rLg8fhr7XF3c37OdyL5CBoBbUmcrs2STEEXkKSicwAihIu+L15I8/1zayzeiUmmI\nzVuKwTq4SDv1J3nk+E7i/tfWUihjMKFlK61EaXT8X9zEmuUeytnRqZjVWp5sKeON3kritAautc3g\nKzGZwx6rzOUgn+gBOYwaoWKGjKbc5Qhh1ArD5d/N+3ioeS9WdJjR8kpHNfn6Cu7NWjxmXTGSdUYs\nKi0bAs3kcbASfSMtSGCGwXrsg8OUCJWaP2cuZKuznZKedkxqDadZksLGPkVheCgiT0FhkuD39NKy\n9zM83W1ExmcTnTUPESIHe3NCDuZh9rNVqVW88OJtPP6be/jnvRvo9ru5yJzFV22ZYZO7c4BllgSW\nWRJGPU6izsDe3q4BJtNSSqpwkK4zjXp8hZFR6rLzUPNeziWTL5GJWqgok3b+0lvCI837uDZpxpic\nN0Kl5tvxOTzQuAeP9DOHWKroZg3VLIuMn7Dl+9EihGCOyTbl+tyeiCgiT0FhEmCv3cXOF3+Lt7cb\nrdaA1+skMiGHWV/7LTrTxM0WaLUavnvBHApfnRrtkgbjwpgMrulax5Ps5XyZiUDwOpVU0c11trER\nEgqDs8ZeTxS6foEHQXPcFTKZdzrrxkzkAVxsy0YrVDzZUsZnvkYMQs150elKrphCWKCIPAWFMCfg\n87Dzf78j2pTMsuXfw2SIpbl9Lx8V38++dx6g8MKbxzWeQ7taOG/4T9iYHo8H8yJt/CSpkAcad/OB\nrANAi4prEgtYFKn0yZwonH4fZrT9Au8AFnS4AsOvoB4OQgi+ZsviyzGZdPk9mFSaEZnwegMB3rHX\n8XFXIxJYZk7gLGuKYuirMCpOnLuzgsIkpW3/BrxOO0tPuplIY1BIJNjyKMq7gPXbn8DrtA9qdxIK\nis7v5N6lScjiT3HdtXnK9qMdjK/aMvlCVDLru4N5V4si47Aq/nETyjyTjVc6qtkv7eSK4N+CV/pZ\nSyNzx2nJUS3EiNMUPAE/P6ssZouzjTyCM/N3ObbzVkct92QtnlQ2LArhxYl3h1ZQmGR4nJ0IoSLS\nFD9ge1RkEsgAXlfXmIu8A7N3snhNWLUsmygsGh1nhJGdxInOSksiBfoo7uktYblMJgoda2mkWbj4\ndXz4Vzy/3lFDibONG5hLnggWcOyTndzl2sIr7dV8PTZrgiM8Or0BP20+NzZNBHpFiIYlJ/adWkFh\nHAj4vAiVCjHCm6A5aTpSBqht2Ex68oL+7VX1xWj1ZvRR49NZQUEhXNGqVPw1azGPNe/j7c46XAE/\nc0wx/CZ+DgXG8K9w/cDeyExs/QIPYJqwMlva+MDeEHYizxPw8/emUl5tr6ZX+jEINRfYMrgqPm9M\nLWsUho8i8hQUxoiOyi1UfvwEXQ2lqDQ64gtWkn3Kd4Y962ZOnEZ05jw+3fIwMx11REelU9O4mf1V\nH5F9yuWoNOPXXUFhciCl5B17Ha+119Duc1NgsHJJXDY5U7jvbKRayzVJM7hmDIssxgqfDKA9yuNY\niwqPHNucwpHwp/rtrOls4GzSmYaVUtnBc60V9Pi93Jgy8e0MFQ6iiDwFhTGgs3ob25+7ldjoHJYU\nfQeX287u0ndwNO5j3qV/HZYwE0JQeOEvKXv/X2zb+RoBnwedMZqc075LyoILxvBTjD1SSqo9PfT4\nfeTozUruUYj4a8MuXmivpJBocolmo6eV97sa+GvmYoomoJ9qea+DD+wNeGSAJeY45hhj+i1oFGCp\nJZ5HXPuolz0ki6AVT5N0UkIrl1pyJzi6gTR5XLzVWcfFTGeVCPaxLSQGk9TyQkcZV8RPJ1bx1Asb\nFJGnoDAGVH36NDZrFmee/EtUfcIlNWEOb3x0Ky2ln5JQeOqwxlPrDEw/6xpyVl2Jr7cHnck64uXf\ncKG818HtNSXsdXcBYFZpuTx+WtgtTU02KnsdvNBeyUXk8gUR7Dv7dZnDH+UW7mvYxb9yl41rPI80\n7eXRln2Y0KBFxZOtZaw0J/Db9HloQuTzONm5MCaDtzvquN2zkYUymHu7kWYSdQa+Ysuc2OAOY39v\nFwFgLgNbAs4ljmfZT1mvQxF5YcSk+gsTQkQLIZ4SQtiFEB1CiH8JIYbsQCqE+IcQIiCE+PFYxqmg\nYK/bRVbKkn6BB2CzZhJlSaWrbteIx1Vr9USYbZNe4Dn8Xn5csQ6n2881zOJXLGB+IJ57G3fxdmft\nRIc3qVnb3UIEKk4jtX+bVqg5nVR299rp8LnHLZYtPW082rKPC8jiHpbxZ07m+xTyiaOZF9oqxy2O\ncCdSreXBnKVcFJdFnc5Bnc7BN+Ky+Ef2Uixj1K1jpMRogxXE9fQM2H7gdaw2vIzQT3Qm20ze00AC\nsArQAY8D/wS+NdiBQogLgcVA3RjGp6AAgDYikm5n64Btfr8XV28nUXrzBEV1kO7mctrKihFCRez0\npRhjxrdS9K3OWrr8Xm5hIdEi+FDIwkKndPNUSzlnWlMHGUHhWKgQBAA/csAN3ksAADUjXyaVUrK5\np42tzg7MQ2h3tbqjlkSMnEdm//LsIhLYLFtY3VHHRbHZI45lqmFRa/leQh7fS8ib6FCOS74+imkR\nFp527+MqqSNDmKmQXfyXfczQW6d03udkZNKIPCFEPnAmMF9KuaVv2zXAG0KI66WUjcc5NgW4t+/4\nN8cjXoUTm/hZp7N30+ukJBSRFFeI3+9h065n8Xh6hr1UG0qkDLDvnQdpKFmNVmtASknFR4+TftJF\nZK349hH7H2p8HEpvvEp3NymY+gXeAQqJ4b/ufaMe/0RmhSWB+xp38TqVfFlmI4SgR3p5hxrmGmOw\njNDTrzfg56aqjRT3tBKJFjd+HmjYzS9SizjzGHYyDr8XGxFH5N/Z0FPp7xpRHAoTixCC36XP42eV\nG7jNW0yEVOEmQLrOxG3pcyc6PIXDmDQiDzgJ6Dgg8Pp4F5AEZ+heOdpBInh3+Q9wl5Ryt5LsqzAe\nZJ58CY6GUt5dexdGgw2P14nP7yb3jO9jtKVNWFxNOz+goWQ1i2ZdyvTMU5BSsnP/G5Ss/S9RqTOI\nyZ4PjL3xcaLWQBNOnNKLURxcjiqniwStIWTnORFJ0hm5Mj6Ph5pLKaGVRGlkNx2oVYI/JM8b8biP\nNO9lW087P2Y2Rdhw4eMp9vH72q3MNkaTpDMeccwsUzT/dJTSKl3EiuD36pF+NtHCbFP0EfsrTA5S\nI0w8NX0l6xwt1Hp6SI8wsTgyHnUInq/eQIDinhYcfh+zjNEkH+W6Uhg6k0nkJQLNh26QUvqFEO19\n7x2LmwCPlPL+sQxOQeFQ1Do9RRffQXv5Zuw121BHmIgvWIHBmjShcTVtW0NS3Ezys0/v3zZr+peo\nathI47Y1xGTP75+9A8bM+PhsayqPN+/n73IHF8lpRBHBJ9SzjkZ+ZCsI+fnCnYCUdAd8GFXqkBQj\nXBafy0yjldc6ghYqXzZk8BVbBvEjFNBSSl5vr2ElKcwRwYR7I1oulXmU0MLbnXX8X/y0I447Nzqd\nF1qruNO3mVUyFT1qPqSeLuHh23HhVTWqMDw0QsUyS0JIx9zS08at1Vto9wfzRgVwbnQa1yfPVIp0\nRsiEizwhxB3AjcfZRQIjuusLIeYDPwaUOWSFcUcIFbacBdhyFgy+8zjhddpJMA98GAshsJgSsLvs\n4xZHrFbPnRkLuK1mC7/ybwCCVWBficng67YTp7pWSsmzbRU83VJOm99NpErDBTEZfDd++qhNZedH\nxjI/MnbwHYcSJ9AV8JLAwFmVCKHGKiPo9HuOepxFreXB7JN4sHE3/+sqx4dkvsnGrQlF5Cq5WwqH\n0OnzcENlMenSzE8oIho9n9PAfzv2k6Q1clm88qNgJEy4yAPuBh4bZJ9yoBEY0NdJCKEGYvreOxrL\ngDig5pBlWjXwFyHEdVLK42b9lr33MBr9wJtafMFK4mecMki4CgrhiTk5j5r9m5jnuwhtX5/NXo+D\n+pYdJM4/b1xjWRgZy0t5p7Gxp5Uev4/ZxhgSdCfWUu3jLfv5V/NelpHELGxUBLr4b2s5zV4Xv04L\nn9+mKiEo0EdR3NvESpmMqu9+Wi0dNOCk0HDsrhKJOgO/TZ+HTwaQkrDoiLDW0cy/m/dT2msnWh3B\n+TFpfDM2JyxiO1F5u7MOjwxwNTMxi2De6OmkUSd7eKm9csqJvDWddayx1w/Y1uMPvfH1hIs8KWUb\n0DbYfkKItYBVCDH3kLy8VQRndNcf47D/AGsO2/ZO3/bBhCU5q67EnDi1LiyFE5vURV9my55PeOvT\n35GfdTpS+tlV9jaoNaTMO2fc49Gp1Cw1h3bJZyhIKdnh6qCkpx2TSsOpUUkjbi4/Upx+H0+3lHEm\naXxDBGdXFxJPgjTwb3spl8dPJy1iyA5RY87lCdP4edVG/spWlspEOnDzNtVkRUSy0jJ4az2NUHGg\nsNcd8PNRVyMNHheZ+khONseP23LcB/YGflWzmVyi+DLZ1PucPNq8j/29Xfwuff64xKBwJI1eF3HC\ngJmBhUFZWPjIV49PBqbUku0Z1pQj+l+XuuxcXvZpSM8z4SJvqEgp9wgh3gYeFkJcTdBC5T7gmUMr\na4UQe4AbpZSvSCk7gI5DxxFCeIFGKaVSwqdwwmGKTWf2RX+g/INHWVvyCADRmXMpOu1KIsyhWdoL\nVzp9Hlq8vcRodNxZt53Pu5sxosGNn7817Obm1NlH3HTHkip3N07pZxEDRe4iEvg3pexydYSVyFtq\nTuCO9Pk81FjKQ55daBCcGpXEtUkz0A3Dt3Gfq4ufVW4ILk+jpRsvaVoTf81aROIYJ9lLKflnYymz\nsPFjZvfPSOZJKw937WKPy06+YXhtB0MdX7Un6DeXrjOdUF1BsiIieV5W0IyLeHFwRn8HbaTpTFNK\n4I0nk0bk9XEJcD/BqtoA8AJw7WH7TAOO91cqxyY0BYXJgSU5jznf/CM+txMhBOoRLpGW9zoodztI\n1BooNFjD9oHU4/fx5/odvGuvx49EjUAiuYoZLCQBJz6eZi+3125l5jGqRMeCqD4rkxZcZHEwP60Z\nFwBWdfiZyq6wJLLcnIAj4CNCqIbdhs4vJb+s3kSkX8v1zCVBGKmSDh70bue2mhL+nrN0jCIP0upz\nU+Pt4Xyy+wUewCLi+Q972NLTNmEib1N3K3fX7+gXeWlaEz9JLmSxOW5C4hlvTo9K5tHmfdzr28qF\nMpuYvpy8jbTwizilH+5ImVQiT0rZySDGx1LK4951BsvDU1CY7HhdXbTuW0fA68aaUYQpNv2o+2ki\nji9mZPHhmQ5Buv1efl1bwjrHwWL3HH0Ud6bPC0u7g9tqtrC5u42vk0s2FnbRzqtUshc7i0UikWi5\nTOazlVZWd9Zx+VGqRMeCZJ2ROcYYXnSWkSxNpIpI2mQvT1FKnEbP/EjbuMQxXIQQI+7CUNLTRr3X\nyc3MJ0EEr5UMYearMpe/u3ZQ4+4Z09lLvSpoBe1gYKGIEx9eJEbVxDwSK3od/KyymGwsXEcRAnjL\nW82NVcX8K3fZCVGkYlRruDdrMb+v3cqDrh1AsNXhj+ILOEcxRx8xk0rkKSgoHJ+mne+zd/V9BPxe\nhEqFDPhJnHU608/68aCt0IZqfHxX/Q62uLpYPv8HpCTMprWjnA1bH+OG6k08kbMsrGb0KnodfNbd\nzPeYwRIRzBvLIQq1VPEy5VwgszALHRFCTQz6cW35BXBLahHXVqznVu8GomUEnbgxq7T8OX3RlFye\n6uirwk08rEr3wOsOn3tMRZ5ZrWVJZBxvdldRIKNJEEY80s9/2Y9aCFaE2BJkqDzXVoEZLT+lCK0I\n/p0WyGh+yTqeba3g5tSiCYlrvMmIiOShnJOpdffg8HvJ1puHPVusMBBF5CkoTBGcbbWUvnEPWSkn\nMX/mxei0RvZXfcz67f/GFJdJ6sILj3rccIyP231u3rc3sHDWt8lKXQJAcvxMlsy9knc++wMlznbm\nmsJnBqrM7QBgNgNjmo2NFyijESdmdNTKburoYYZhfAutknRGnpq2ko8djZT3OkjSGTnNkoRRPTVv\nzfn64FJoMc2cwsH8x2KaiRAqssah5d/1yTP5Yfk6fulbR7o004oLF35uSSka9+KbA+x3OSggul/g\nQbBQZYaMpsx14nUGSQ2jXNTJztS8kygonIA0bn8Hnc7ESXMuR923nJaXdRrN7XtpKHnrmCLvgPEx\nMKjxcbO3F4kkLiZnwPa46ODrBo+LuWF0f47XBPuqVtFNAQc7LFQRFH81dFMpHbxFNek6E6dFjb9Z\ntValYlVUMqsmLt9/3EiNMHFmVDLP2PfRJnv7ls87eJ9avmnLwTzCZeDhkKgz8sS0Fbxjr2OPy06M\nJoEvWlMnVFgk6PTs7+1GStk/Ey6lpIpu0sMwBUJh8qCIPAWFKYKnuwOzKbFf4B3Aak6hprkkJOdI\n1hrQCBX1zTuwWQ+aFje07AQgMyLymMdKKXnXXs8bHbV0+jzMNFn5hi17TJfnZhmjyYkw8x/3Hi6X\nBeQQxU7aeZ79GISaJ+VeVASX6a5LKlSWhsaBm1JmE6OJ4JX2Gt6QVUSptFwZO31cO2AY1UHT6XDh\nwpgMfty1nqfZx/kyE4HgdSqpwsG1MSdeBxiF0KGIPAWFKUJkQjbluz+ix9WOyRADgJQBapq2EJmQ\nM8jRQ8Oi0XFudCqvlf4PIVSkJBTR1lnBlp3PMMtko+A4lYn3NOzkxfYq8rGSRCTvuxt5u6OO+7NP\nIm+MKhqFEPwhfT43VG3kDs/m/u2FBit3ps8HIdAL9ZRdHg1HdCo1P0qawfcS8nD4vURpdFMy/3A4\nzI+M5drEGTzYuJv3qAVAi+CHiflhV127taedN/ra5RUYrFwQk45N+//t3Xd8XNWVwPHfmaaZkTTq\n1ZIsybbcbdzB9OI0Oht6dsmSZJckBEKWlCXJJiEJYBK6s7ukLJAAwbQACaEYSCDENRhjG3dbtiWr\n2Op1NO3uHyMLS9hW8YxGGp3v5zMfS09v3jtzPeXMffee64x1WOoY9J1NqREmFAwQ8LZicyZhGcTl\nq5yZ51Gx5nleX3kXsyZdSIIjiR37/kpdw25mnnd7xOK7OXcaIQMvb32G9VuWA3BKcg7fGzfrmJMu\ndna28FzDPq5mEkukEACvCXCnWc+y6q08VHpyxOLrqyAhkccnncH69nqqfB2UOJOY4UobURNE4lXA\nhGgJ+km22D+2moTDYiXjiJ7Tcm8rGzoaSLLYWJycQ+IYS7yvyCxhSWo+a1oPAbAoOStmYwSP5cm6\n3fyiZhs5uMjFzZNte3i+YR/LSk4elvGUavDG1qtIqRHMhILsW7mcqvdewu9txZaQSN6c8yk+7Vos\nA/jAszuTmX3NXex8bRl/f/9XALhScpl28XdIL5l71Pvce2sNczNLMOtWsOr6jQOK02Gx8u1xM/m3\nnDL2d7WTY3f2W8R2ZWstbmycfcRge6fYONcU8GjHNjqCgaj2pllEmB+hdVxV/4LG8LtDu3i6rpzm\nkJ9Ei41L0ov4UvbkjyV7ARPijsoPeK25CiFcyDTRYuMHBSdxaoxmu8ZKmi2BT6WNzHIhNb5O/qdm\nG5+iiMuZgIjQYnwsDa7nvuoPebAkel/U1NBpkqfUCLH7rd9Qtf6PTCk5j5zMqRxs2MHWtc8R6Gim\n7NM3DegY7vRxzL76TnztjQT9XThTspGjXAo7nNx1fOtpVvYz2eJY0mwJA+5pEAkXIO4r1L1NO9Xi\ny//WbOOp+j2cTQHTSGNXqJnldeU0Brr4bsFJvfZ9/NBu3miu5vNMYTG5f7dhBAAAIABJREFUtODj\n8dAOvlexnqfLziZLLwWOCO+01GDFwkUU9/SCe8TBJ00Rj7ZvoyXoH3L9RBU9Y3sghFIjhK+jmar3\nX2b25MtYMPNzFOXNY/70q5k37UqqN62gq6VuUMdzJKbhSs39WII3+6Im/nqXiznlu+j41tJ+Z9NG\nyunJOXQS5HUqera1Gz9vUMGCxExcMSpCO1RdoSDvtNTwWlMlNb7OWIczorQEfDzbsJcLKOZaKWOO\nZHG5TORKJvFK0wGqfR299v9Dwz5OJ48zJB+bWEgXJ19kGmKEV5oqY/QoVF8BQlgAW5+0IYHwJfeg\nCcUgKtWf0fXOqlScaj9YjgkFKB63sNf24vxF/GPzk7TW7iLBM3ovN5Y4k7k2s5Qn6nbznjlINm42\nU4/FItyUNy3W4Q3KytaD3F6xgdaQHwh/U74svZib86b1WiprNPOGgrzZXMX+rnbyHW7OS8kjcYC9\nNHu62vCZEAvI7rV9Adk8wQ62dTb3LB3XFQxQF+jiEJ28aSpZRA5JYsctNrJwcsjvPdopVAycnJTN\nL9jG21RxLuFLyn4T4i0qmez0kGp1xDhCdTSa5Ck1Atjd4dmlLW01eJJye7a3tFUD4HCnxiSuSPpy\nzhRmudN5ubGCpoCPixOL+Kf0YnKHuHZuLNT4Ovnu/veYatK4ikkk4+AdqnimYRcFCW4uzyjp/yAj\n3F5vKzfvXUN9oItMcVJnvDxcs417SxYNaF3XtO41eWvoZBwfldSppaPX32t9ndxUvhqAKjrYxk6e\nYzdfMzNJx8kB2pnk1FUoR4pSZzKXpBXxROMONpt6cnGzgToa8HJv7iKdyDRCaZKn1AiQmFVMcs5E\n1n34JInuTNI8BTS3VrN28xMkZhSSnD851iGeMBHhNE8Op43iwfR/bqrAaoR/ZzpOCb99fooi9plW\nnq/fN+qTPGMMP6zYQELAxp3MIRs3DXj5RWgT39+/nuVlZ/XbWzk+IYnprlSe6dxFtnFRKEnUmg6e\nYAcFdjez3OHyPksPbKLTH+J2FlIgSbQYHw/zIQ+xiWTsZNtcLEnNH46HrQboP/JnMMWVwh8bK9jo\nr2OGO5VrsyZErQSSOnGa5Ck1AogIUy76Fpue/j5//MttJCQk09XVSkJSJjMvvl2/JY8Qtb5OcnH3\nJHiHlZDMBv+hGEUVObu7WtnZ1cLXmUW2hC+ppouTq00Zd/jfY2NHAycNYNm6HxTO4ZbyNfzAv5Zk\nY6cVP5nWBO4ZvxCLCHV+L2vaD3E9UymQcG+fRxxcZybzHVaTn+DirvELRt1YzXhnEeHC9CIuTC+K\ndShqgPQVpNQI4U4fx4IvPUz9ztV0NBzAlZpHZtkpWGw61mWkKHEm8yoHaDJdpEp4ZrExhk00UJIw\nsuqE7fa28HJjJY2BLqa4UvlMWkG/y4a1BMLjDDPpfQk9k/AM1+agf0DnHte9Ju+7rbXs62oj3+Hm\nTE9uz4oircHD5+k9czYdJwL8U0Yx43Q5L6VOmCZ5SgGhgJ+Ktc9Ru+kN/J2teMZNpWjxlaSMG94l\nhSxWO1lTTh/Wc6qB+0xqAY8f2s29wQ1cYkrx4OBtDvAhDfwk6+i1CE+UPxTCJjKo3tyXGvZzd9Um\nPDjIxsWbzdU8VbeHZaWnHDd5muTykCAWVpsaLuOjVVJWU4sFYZpr4GND7RYLZx9jLeACRyKpVger\ng7VMOWJN4TXUYghP/Hi7pYb5iRkDnvChlPo4TfLUmGeM4cMXfkpT+fuUFiwmKTeLvVVr+eDJ7zDr\nyh+TWjQr1iFGxMcLH+vLf7A8NgcPlCzijsqNLPNuAiDV6uCbOTOOmdAM1cuNFfzu0G4qfO2kWh1c\nkl7E57MmfayYcF91fi/3VG3mTPK5hjJsYqHeeLk7sJ57qzZzT/HCY9432WrnqsxSHju0iybjYypp\n7KKZt6nikvSiiNWss1ssfD57IvdXb6HTBDiJTPbTygoqsSI8VLMVAJdYuSV/OuenFfZ7zK5QECBu\n1h/2h0L4TUiX3FMnRJ89asxrrthEw+51nLXgJory5wMwY9L5vPruTyl/+zHm/PM9MY7w+Dobq/F7\nW0nMKMLq+PiHcCQKH6uPTHB6+M3E06joaqcjFKAkIQlHhBOLZ+rLub96C/PI4jwKqQi28btDu6ny\ndfCDwjnHve9fW2owwGeZ2LMmbIY4+ZQZz+Nt22kL+kk6Tu/YF7PL8FjtPFVXzruBatKsDr6YMYnP\nZU2M5EPks+nFJIiV3x3azTr/QVxiJWQM51LApykihOFFU86dBzYyPiGJGe60ox5nj7eVZdVbWdt+\nCAMsSMzkxrypTHR6IhrvcGkIdPFQ9Rb+0lyNH8Nkp4d/zxl5a9iq0UHf8dWY17h3A05nCoV583q2\nWSw2Jo0/i1UbfkPQ78U6AqvudzZWs/3le2k+sAUAm8NNwcmfpejkKxARZl/UxHVl3nDh47ufHnTh\n43q/l62dzSRb7cx0p8VNDbhIKUxIjMpxfaEgj9Tu5Azy+Lx8NFygwCTxaPM2rsuaSPFx1gntCgWx\nYyGhT9HaJOwYwNdP0VqLCFdllnJlRgmdoSBOizUq//ciwkXpRVyYVojXBPmv/e9T3dbJNUzquTT9\nr2Yqu2jmD/X7jprk1fg6+cqeVSSF7FxLGQK82X6Ar+xZxSMTTx/SuL5ybyvPNuxlT2cruQ4Xl6aP\nZ1Zi+ok+3AHpCgW5cc9qmnw+LqaUFBy8663m1n3ruL94IfN0aT41SJrkqTHPancSCPgIhvzYjijo\n2eVrQyw2ZATO8AsFfGx86jZsQeGM+TeS5M6kvHIVW9/5LTaHm3HzLhz6sY1hWc0Wnq3fR7B72bF8\nu5vbi+YwdRBjstTQVPo6aA75OYXcXttPIZdH2cbGjsbjJnnzkzL579ptrKKW0whfQg4Zw9scoCQh\nibQBFq0VkWG5VCgiuMRGja+TEjy9xh5aRCgxHqr9R19V5Jn6ckzIcBvzSJRw7+Qik8ttoVU8XVfO\nLfnTBxXL2rZDfGvvP0jCzmRS2dTZxOvNVXw7fyYXDcOM0jeaq9jna+spKwNwisnlDt7j0YO7NMlT\ngzbyPr2UGmZZU06n/J3fsmHrs8yddiUWi5WWthq27nmNrMmnYhmBY2IObX8Xb8tBLj7nLlKSw7XE\nMtNK6fK1UbnmOfLnXjDkY/++bg9P1+/lUkpZTC71eHnKv5NvlK/lmclnH/dSnzpxSd3Ptwa6em1v\nILz6Q38zZCe7UliSks+jzdvYahrIJZH3OcR+2liaO39I5XiMMWzqaGR9ez0ui41zUvIivqZsiTOJ\nLb5GQsb09Bz6TYjtNHGm8+i1FTd1NDKDjJ4ED8AtNmaZTDZ1NA7q/CFj+NmBzUwihZuZhb378vFv\n2cYD1Vs4JyUv6s/9DzuaKCKpJ8GDcKK7wGTzfMeeqJ5bxaeR9+ml1DBzpeUx4ZwvsOWtX1NetYZE\nVwb1jXtwerIpPfv6WId3VO11+3G7M3sSvMPys2eyp/LvhIa4HJQxhmfq93I6eVwgxUC4rMWNZhbf\nDK1kRVMVl2aMP8Ho1fFk213MdWfwYkc5xSaZPEmk1fh4nO14LHYWJ2f3e4zvFcxmsiuFPzbsZ3Og\ngamuFG7Nns6cAdS468sXCvL9/et5t+0gidjwEeIXNVv5Zv6MiNZLuzKzhBtaVrGMTXzKFBHE8DJ7\nacPPP6UXH/U+KVYHB/n4c/0QnaQMMiHb09VKlb+DayjDLuExlhYRLjIlvGOqWdtWxzkRnlzTl8dq\np4Eu/CaE/Yh1p+vwDvrxKAWa5CkFQMGCS0kpnEnt5rfwe1uYMG8JOdPPwZYwMmt1OT3ZdHY20tHZ\niNv10ViluqY92F0eLPYE6NMTNBB+E+JQwMuF9L4smyYJZOPkgK/9REMf0XZ7W3ipoYKDgU4mJni4\nKIIzSgfjPwtmcVP5ar7rX0MOLurxYhMLS4vmD2j2qE0sXJ1ZytWZJ74s2ON1u1nddogvM4N5ZNFF\nkOXsYmnVJmYlpjM+Ian/gwzAdHcaPy6cw/3VW7grsB6APLuLpfnzKT3G5enz0wr4btt6Xjf7OYcC\nBPgrVWyniR+lHX+CSl8hEx6aYKF3T+fh30PdQxei6VOp43i8bjdPsZPLzQQSsPIB9bxDFdek6xJv\navA0yVOqW3LuRJJzIzuDMFqyp51J+duP8fY/lrFo1r+Q5M6mvHIl2/e+SeHJlyNy/DIbx2IXCzk2\nF9sDjT3juQDqTCe1dFIYoQ/0kei1pkp+UvkBKSSQTyJr2cPT9eU8UHLygNZsjaT87mLCbzVXs9vb\nQpbdxSdS80mzJQxrHAB/aqjkNPJYIOEeRBc2rjVlvM8h/txYyZdzp0TsXGel5HGaJ4dd3lYswESn\n57iTPs705HJFejFPNeziJfYiQDsBLksfz7mD7HWb4PSQY3PyamA/k0wKVrFgjOHP7MMhFhYkRn88\nXLEzmVvzZ3BP1YespBonNprxsSAxk+siPLtZjQ2a5Ck1CtkSEplx+Q/Z+sKd/Omv3+/ZnjPjXMYv\nvnrIxxURrsos4YGaLaSZBE7pHpP3LLtJtTo4LyU+1xJtDfq5+8BmFpHLvzIFm1hoM37uCb3Pzw5s\n4jcTTxv2mBIsVj6dVjDs5+2rKegjm9492naxkG6cNAV9ET+fTSwDTqpFhJvzp3N+eiHvtNQCcFpy\nNmVDSMqtItySP4Pv7n+P77GGaSadclrYSys35UwjZZhWnrkkfTwnJ2XzZnMV7aEA8xIzmJuYoUsb\nqiHRJE+pUSpl3FQW3vB/NO3bEF6lI38yrrRwEnYihY8vzyimKejj93V7+JPZB0CJI4n7ixaRGKVJ\nKC0BH785uJM3mqroMkEWJGXyhZyyYat1tqr1IF4T5HIm9NSWSxI7F5hifuHdzAFfx5hdZmu6K5V/\ndBzkE6awp1et2rSzn1auchXHNrhuE52eiDxXTvfk8HDpYpbXl7PH28I4u4ubMqYOe426XIeLa7Mm\n9L+jUv3QJE+pUcxitZFeOr/n90gUPhYR/i1nMldllrKju05emdMTtZ4EbyjIV8tXU9PVyRnk48bG\nytYabmhbyS8nnHrM8ViR5O+uHZdA7/Fuzu63SF/3agrx4MOORn5Vu6N7pqyVJan5fCl78jF7qq7L\nnsgte9dwLxs4zeTRgp/X2E++3c2S1Pjr2Z3qTuWH7sGN51NqpNIkT6k48VGCt3TQhY+PxmO1M38Y\n6nK93nSA8q5WfshCCrtLR5xnCvihWcejB3dye1F01oQ90rzEDCzAm1RyAcVAeCD+W1SSY3NRFCdj\nEbd2NHFj+WpyjJvLmUBryM/rDQfY1N7ILyecetRJHfOTMlk6fj7/W7OdX3ZtwYJwhieHm/Om4RoB\nNSQDJsSbzdW821KLwXBqcg7npeT3u/ybUmNB7F+hSqkxbX17PRNI6UnwAJxiY6HJ4e/tVcMSQ67D\nzVWZpTxZt4fdpplCktlMPfto5fa8uVjjZDzU/x3cSZZx8T3m95ToWGCy+WHXOl5s2M8VmSVHvd/i\n5BxOScqmNejHYbHiHCHrw/pDIb61bx1r2+uYgAdB+EnLB7zSVMnPxy+I+HJzSo02muQppWLKbbHR\nig9jTK9Lwq34cA9jT9FXcqZQnJDEC/X7We2vZqLLwzcyp8XVKgMb2hv4NEW9arAVSTIFJpEHa7bg\nN6FjjgUTETzDNPlgoF5uquAf7XX8BycxXcJLj201jdzTvoGXGiv4bEZxbANUKsY0yVNKxdSS1Hxe\nbNzPq+znk6YIiwjbTSOrqOHa1OEbfC4inJ9WyPlphcN2zuGWaLXRFOg9IzZkDO0EKCKZ/67dxgRn\nMicPoODySPBWczXTSe9J8ACmShqzTAZvNVdrkqfGPE3ylIoDsy9qAsCsWxHjSAbvJHc612SW8mTd\nbt6iEpexUUk7s11pXJulBWAj6ZOp43imrpx5JospkkbAhHiJvTTSxY3M5Hds54WG/aMmyfOHQiQc\n5WMsASutoUAMIlJqZNEkT6lRbPZFTVxX5mVO+S46717PqldsjLaXtYjw1dypnOnJ5Y3mKnyhEDck\nTeZ0T05POZPhcMDXwV+aq/GGgsxPymS2Oy3uapNdlzWRje0N3N35PlnGhZcArfi5lFJKxEORSaJ2\nFK1qcnJyFo927qLWdJAj4RI3B00nGzjEtclagkSp0fVpoJQC4iO562uGO40Z7rT+d4yC5XXlPFSz\nBQdWErDwyKGdnJ6cw48L58bVLE231cZDpSdz6951bGxv4GzGcQp5FEoSfhPiQxpZ5Bo9YxAvzSjm\n1aYD3O5bxyKTgyCsoZZMu1Mv1SoFxM+7l1JjzNzumZCRKJcylm3vbObBmi0soZD7OY17OY0vM4OV\nrQd5qn5PrMOLOJtYuDlvGkZgDy3U42WjqeM+NtCCjyuPMcN2JPJY7fxP6WIuzRzPLnsTO+2NXJxR\nyMMTFg/bChVKjWSjKskTkTQReUJEmkWkUUR+LSKJA7jfVBF5UUSaRKRNRNaISOzXC1JKxdwrTZWk\nkcDlTCBBrFhEWCDZnEwOLzdUxjq8qChxJnPP+IX4HEEeZCP3s5EOh5+fFS8YtlVGIiXV5uCruVNZ\nPvlslk8+mxvzpsVkjV+lRqLR1gXwJJADnAs4gEeBh4HPHesOIjIB+BvwK+D7QCswHfBGOVal1CjQ\nHPCRgRNrn/F/WbjYGKyPUVTRNzcpg8cnnUGlrwODodCRGHdjEJUa60ZNkiciU4BPAvOMMe93b/sa\n8LKI3GqMqTnGXX8CvGyM+c8jtpVHN1ql4ktjoIud3hbSrAlMdCbHVTIw3Z3GG83VHDQdZHcP3g+Y\nEO9xiBnu1JjG1hUK8l57Hb5QiDmJGRG/BCkiFCb0ezFEKTVKjZokDzgFaDyc4HV7AzDAIuDFvneQ\n8CfR+cDdIvIqMIdwgnenMeZj+yuleguYEA9Vb+WFhn0EMABMdnr4UeHcuEkOPp1awPK6PSz1v88S\nU0giNv5GNdW0873sWTGL6+2WGu6s3EhryA+AHQvX50ziX7ImxiwmpdToMprG5OUCB4/cYIwJAg3d\nfzuabCAJ+DbwZ2AJ8AfgeRE5PXqhKhUfHj24i+cb9nExJdzJyXydWbR4A9yydw3+UCjW4UVEotXG\nspJTOMmTxnPs5hG24XAK9xYvjNls373eVr6/fz1loVR+yiLu4VTOpYCHa7fzVnN1TGJSSo0+Me/J\nE5E7CSdhx2KAqUM8/OEk9gVjzIPdP28UkcXADYTH6h3T7jd/hc3p7rUte+qZZE87a4jhKHXiDpdP\nMetW0PnMeqL1Mg6YEM/W7+U8CjhfigHIwU26cfJf/rX8rbWWc1LyonLu4ZbjcPGTonn4QkECxuC2\nxvat8YXG/SRh59+Y3rME2RVMZJ9p5Zm68rhpd6XGq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4wxD4tICOj75rrPGFM6vNEqpcYy\nTfKUUkoppeKQTrxQSimllIpDmuQppZRSSsUhTfKUUkoppeKQJnlKKaWUUnFIkzyllFJKqTikSZ5S\nSimlVBzSJE8ppZRSKg5pkqeUUkopFYc0yVNKKaWUikOa5CmllFJKxSFN8pRSSiml4pAmeUoppZRS\ncej/ASL/k9zTYYG+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12608908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with dropout\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-0.75,0.40])\n",
    "axes.set_ylim([-0.75,0.65])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Note**:\n",
    "- A **common mistake** when using dropout is to use it both in training and testing. You should use dropout (randomly eliminate nodes) only in training. \n",
    "- Deep learning frameworks like [tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/dropout), [PaddlePaddle](http://doc.paddlepaddle.org/release_doc/0.9.0/doc/ui/api/trainer_config_helpers/attrs.html), [keras](https://keras.io/layers/core/#dropout) or [caffe](http://caffe.berkeleyvision.org/tutorial/layers/dropout.html) come with a dropout layer implementation. Don't stress - you will soon learn some of these frameworks.\n",
    "\n",
    "<font color='blue'>\n",
    "**What you should remember about dropout:**\n",
    "- Dropout is a regularization technique.\n",
    "- You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.\n",
    "- Apply dropout both during forward and backward propagation.\n",
    "- During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5.  "
   ]
  },
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   "source": [
    "## 4 - Conclusions"
   ]
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   "source": [
    "**Here are the results of our three models**: \n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **test accuracy**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN without regularization\n",
    "        </td>\n",
    "        <td>\n",
    "        95%\n",
    "        </td>\n",
    "        <td>\n",
    "        91.5%\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with L2-regularization\n",
    "        </td>\n",
    "        <td>\n",
    "        94%\n",
    "        </td>\n",
    "        <td>\n",
    "        93%\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with dropout\n",
    "        </td>\n",
    "        <td>\n",
    "        93%\n",
    "        </td>\n",
    "        <td>\n",
    "        95%\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
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   "metadata": {},
   "source": [
    "Note that regularization hurts training set performance! This is because it limits the ability of the network to overfit to the training set. But since it ultimately gives better test accuracy, it is helping your system. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations for finishing this assignment! And also for revolutionizing French football. :-) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<font color='blue'>\n",
    "**What we want you to remember from this notebook**:\n",
    "- Regularization will help you reduce overfitting.\n",
    "- Regularization will drive your weights to lower values.\n",
    "- L2 regularization and Dropout are two very effective regularization techniques."
   ]
  }
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